| Title/Authors | Title | Research Artifacts
[?] A research
artifact is any by-product of a research project that is not
directly included in the published research paper. In Computer
Science research this is often source code and data sets, but
it could also be media, documentation, inputs to proof
assistants, shell-scripts to run experiments, etc.
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Dialog-based Language Learning Jason Weston |
Dialog-based Language Learning Details |
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Linear Contextual Bandits with Knapsacks Shipra Agrawal, Nikhil R. Devanur |
Linear Contextual Bandits with Knapsacks Details |
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Amit Daniely, Roy Frostig, Yoram Singer |
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Interpretable Nonlinear Dynamic Modeling of Neural Trajectories Yuan Zhao, Il Memming Park |
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories Details |
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Deep ADMM-Net for Compressive Sensing MRI Yan Yang, Jian Sun, Huibin Li, Zongben Xu |
Deep ADMM-Net for Compressive Sensing MRI Details |
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Optimal Sparse Linear Encoders and Sparse PCA Malik Magdon-Ismail, Christos Boutsidis |
Optimal Sparse Linear Encoders and Sparse PCA Details |
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A scaled Bregman theorem with applications Richard Nock, Aditya Krishna Menon, Cheng Soon Ong |
A scaled Bregman theorem with applications Details |
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The Power of Optimization from Samples Eric Balkanski, Aviad Rubinstein, Yaron Singer |
The Power of Optimization from Samples Details |
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Stochastic Structured Prediction under Bandit Feedback Artem Sokolov, Julia Kreutzer, Stefan Riezler, Christopher Lo |
Stochastic Structured Prediction under Bandit Feedback Details |
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Probabilistic Linear Multistep Methods Onur Teymur, Konstantinos Zygalakis, Ben Calderhead |
Probabilistic Linear Multistep Methods Details |
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Learning Tree Structured Potential Games Vikas K. Garg, Tommi S. Jaakkola |
Learning Tree Structured Potential Games Details |
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Sampling for Bayesian Program Learning Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum |
Sampling for Bayesian Program Learning Details |
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Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu, Dilin Wang |
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Details |
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Improved Error Bounds for Tree Representations of Metric Spaces Samir Chowdhury, Facundo Mémoli, Zane T. Smith |
Improved Error Bounds for Tree Representations of Metric Spaces Details |
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Variance Reduction in Stochastic Gradient Langevin Dynamics Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabás Póczos, Alexander J. Smola, Eric P. Xing |
Variance Reduction in Stochastic Gradient Langevin Dynamics Details |
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Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint Nguyen Cuong, Huan Xu |
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint Details |
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Multivariate tests of association based on univariate tests Ruth Heller, Yair Heller |
Multivariate tests of association based on univariate tests Details |
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Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods Antoine Gautier, Quynh N. Nguyen, Matthias Hein |
Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods Details |
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Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy Aryan Mokhtari, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Alejandro Ribeiro |
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy Details |
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Online Convex Optimization with Unconstrained Domains and Losses Ashok Cutkosky, Kwabena A. Boahen |
Online Convex Optimization with Unconstrained Domains and Losses Details |
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Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos |
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations Details |
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Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences Hongseok Namkoong, John C. Duchi |
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences Details |
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Full-Capacity Unitary Recurrent Neural Networks Scott Wisdom, Thomas Powers, John R. Hershey, Jonathan Le Roux, Les E. Atlas |
Full-Capacity Unitary Recurrent Neural Networks Details |
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Designing smoothing functions for improved worst-case competitive ratio in online optimization Reza Eghbali, Maryam Fazel |
Designing smoothing functions for improved worst-case competitive ratio in online optimization Details |
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Even Faster SVD Decomposition Yet Without Agonizing Pain Zeyuan Allen Zhu, Yuanzhi Li |
Even Faster SVD Decomposition Yet Without Agonizing Pain Details |
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Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters Zeyuan Allen Zhu, Yang Yuan, Karthik Sridharan |
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters Details |
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The Multiscale Laplacian Graph Kernel Risi Kondor, Horace Pan |
The Multiscale Laplacian Graph Kernel Details |
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Backprop KF: Learning Discriminative Deterministic State Estimators Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel |
Backprop KF: Learning Discriminative Deterministic State Estimators Details |
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Chuan-Yung Tsai, Andrew M. Saxe, David D. Cox |
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PAC-Bayesian Theory Meets Bayesian Inference Pascal Germain, Francis R. Bach, Alexandre Lacoste, Simon Lacoste-Julien |
PAC-Bayesian Theory Meets Bayesian Inference Details |
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Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov, Michael Chertkov |
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models Details |
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Andrej Risteski, Yuanzhi Li |
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Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis Weiran Wang, Jialei Wang, Dan Garber, Nati Srebro |
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis Details |
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Architectural Complexity Measures of Recurrent Neural Networks Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio |
Architectural Complexity Measures of Recurrent Neural Networks Details |
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Flexible Models for Microclustering with Application to Entity Resolution Brenda Betancourt, Giacomo Zanella, Jeffrey W. Miller, Hanna M. Wallach, Abbas Zaidi, Beka Steorts |
Flexible Models for Microclustering with Application to Entity Resolution Details |
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Catching heuristics are optimal control policies Boris Belousov, Gerhard Neumann, Constantin A. Rothkopf, Jan Peters |
Catching heuristics are optimal control policies Details |
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Deep Learning without Poor Local Minima Kenji Kawaguchi |
Deep Learning without Poor Local Minima Details |
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Feature-distributed sparse regression: a screen-and-clean approach Jiyan Yang, Michael W. Mahoney, Michael A. Saunders, Yuekai Sun |
Feature-distributed sparse regression: a screen-and-clean approach Details |
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Edge-exchangeable graphs and sparsity Diana Cai, Trevor Campbell, Tamara Broderick |
Edge-exchangeable graphs and sparsity Details |
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Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition Shizhong Han, Zibo Meng, Ahmed-Shehab Khan, Yan Tong |
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition Details |
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Stochastic Optimization for Large-scale Optimal Transport Aude Genevay, Marco Cuturi, Gabriel Peyré, Francis R. Bach |
Stochastic Optimization for Large-scale Optimal Transport Details |
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Unsupervised Domain Adaptation with Residual Transfer Networks Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan |
Unsupervised Domain Adaptation with Residual Transfer Networks Details |
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LightRNN: Memory and Computation-Efficient Recurrent Neural Networks Xiang Li, Tao Qin, Jian Yang, Tie-Yan Liu |
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks Details |
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Learning Bayesian networks with ancestral constraints Eunice Yuh-Jie Chen, Yujia Shen, Arthur Choi, Adnan Darwiche |
Learning Bayesian networks with ancestral constraints Details |
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A Bayesian method for reducing bias in neural representational similarity analysis Mingbo Cai, Nicolas W. Schuck, Jonathan W. Pillow, Yael Niv |
A Bayesian method for reducing bias in neural representational similarity analysis Details |
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An Efficient Streaming Algorithm for the Submodular Cover Problem Ashkan Norouzi-Fard, Abbas Bazzi, Ilija Bogunovic, Marwa El Halabi, Ya-Ping Hsieh, Volkan Cevher |
An Efficient Streaming Algorithm for the Submodular Cover Problem Details |
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Sublinear Time Orthogonal Tensor Decomposition Zhao Song, David P. Woodruff, Huan Zhang |
Sublinear Time Orthogonal Tensor Decomposition Details |
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Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods Lev Bogolubsky, Pavel E. Dvurechensky, Alexander Gasnikov, Gleb Gusev, Yurii E. Nesterov, Andrei M. Raigorodskii, Aleksey Tikhonov, Maksim Zhukovskii |
Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods Details |
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Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula Jean Barbier, Mohamad Dia, Nicolas Macris, Florent Krzakala, Thibault Lesieur, Lenka Zdeborová |
Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula Details |
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Dynamic matrix recovery from incomplete observations under an exact low-rank constraint Liangbei Xu, Mark A. Davenport |
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint Details |
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Finding significant combinations of features in the presence of categorical covariates Laetitia Papaxanthos, Felipe Llinares-López, Dean A. Bodenham, Karsten M. Borgwardt |
Finding significant combinations of features in the presence of categorical covariates Details |
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Long-term Causal Effects via Behavioral Game Theory Panagiotis Toulis, David C. Parkes |
Long-term Causal Effects via Behavioral Game Theory Details |
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Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm Kejun Huang, Xiao Fu, Nikos D. Sidiropoulos |
Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm Details |
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Rishi Gupta, Ravi Kumar, Sergei Vassilvitskii |
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Composing graphical models with neural networks for structured representations and fast inference Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Ryan P. Adams, Sandeep R. Datta |
Composing graphical models with neural networks for structured representations and fast inference Details |
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Safe Exploration in Finite Markov Decision Processes with Gaussian Processes Matteo Turchetta, Felix Berkenkamp, Andreas Krause |
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes Details |
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Data driven estimation of Laplace-Beltrami operator Frédéric Chazal, Ilaria Giulini, Bertrand Michel |
Data driven estimation of Laplace-Beltrami operator Details |
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Statistical Inference for Pairwise Graphical Models Using Score Matching Ming Yu, Mladen Kolar, Varun Gupta |
Statistical Inference for Pairwise Graphical Models Using Score Matching Details |
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Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain Timothy N. Rubin, Oluwasanmi Koyejo, Michael N. Jones, Tal Yarkoni |
Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain Details |
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Optimistic Bandit Convex Optimization Scott Yang, Mehryar Mohri |
Optimistic Bandit Convex Optimization Details |
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Probing the Compositionality of Intuitive Functions Eric Schulz, Josh Tenenbaum, David Duvenaud, Maarten Speekenbrink, Samuel J. Gershman |
Probing the Compositionality of Intuitive Functions Details |
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Adversarial Multiclass Classification: A Risk Minimization Perspective Rizal Fathony, Anqi Liu, Kaiser Asif, Brian D. Ziebart |
Adversarial Multiclass Classification: A Risk Minimization Perspective Details |
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The non-convex Burer-Monteiro approach works on smooth semidefinite programs Nicolas Boumal, Vladislav Voroninski, Afonso S. Bandeira |
The non-convex Burer-Monteiro approach works on smooth semidefinite programs Details |
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Clustering Signed Networks with the Geometric Mean of Laplacians Pedro Mercado, Francesco Tudisco, Matthias Hein |
Clustering Signed Networks with the Geometric Mean of Laplacians Details |
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Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning Wouter M. Koolen, Peter Grünwald, Tim van Erven |
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning Details |
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A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization Jingwei Liang, Jalal Fadili, Gabriel Peyré |
A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization Details |
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Data Poisoning Attacks on Factorization-Based Collaborative Filtering Bo Li, Yining Wang, Aarti Singh, Yevgeniy Vorobeychik |
Data Poisoning Attacks on Factorization-Based Collaborative Filtering Details |
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Unsupervised Learning of Spoken Language with Visual Context David F. Harwath, Antonio Torralba, James R. Glass |
Unsupervised Learning of Spoken Language with Visual Context Details |
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Adaptive Skills Adaptive Partitions (ASAP) Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor |
Adaptive Skills Adaptive Partitions (ASAP) Details |
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High resolution neural connectivity from incomplete tracing data using nonnegative spline regression Kameron D. Harris, Stefan Mihalas, Eric Shea-Brown |
High resolution neural connectivity from incomplete tracing data using nonnegative spline regression Details |
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Residual Networks Behave Like Ensembles of Relatively Shallow Networks Andreas Veit, Michael J. Wilber, Serge J. Belongie |
Residual Networks Behave Like Ensembles of Relatively Shallow Networks Details |
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Error Analysis of Generalized Nyström Kernel Regression Hong Chen, Haifeng Xia, Heng Huang, Weidong Cai |
Error Analysis of Generalized Nyström Kernel Regression Details |
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Select-and-Sample for Spike-and-Slab Sparse Coding Abdul-Saboor Sheikh, Jörg Lücke |
Select-and-Sample for Spike-and-Slab Sparse Coding Details |
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End-to-End Kernel Learning with Supervised Convolutional Kernel Networks Julien Mairal |
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks Details |
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Relevant sparse codes with variational information bottleneck Matthew Chalk, Olivier Marre, Gasper Tkacik |
Relevant sparse codes with variational information bottleneck Details |
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Single-Image Depth Perception in the Wild Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng |
Single-Image Depth Perception in the Wild Details |
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DECOrrelated feature space partitioning for distributed sparse regression Xiangyu Wang, David B. Dunson, Chenlei Leng |
DECOrrelated feature space partitioning for distributed sparse regression Details |
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The Parallel Knowledge Gradient Method for Batch Bayesian Optimization Jian Wu, Peter I. Frazier |
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization Details |
Author Comments:
The arXiv version contains minor edits and typo fixes. Please cite "J. Wu and P. Frazier. The parallel knowledge gradient method for batch bayesian optimization. In Advances In Neural Information Processing Systems, pp. 3126-3134. 2016".
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Stochastic Online AUC Maximization Yiming Ying, Longyin Wen, Siwei Lyu |
Stochastic Online AUC Maximization Details |
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Crowdsourced Clustering: Querying Edges vs Triangles Ramya Korlakai Vinayak, Babak Hassibi |
Crowdsourced Clustering: Querying Edges vs Triangles Details |
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Data Programming: Creating Large Training Sets, Quickly Alexander J. Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré |
Data Programming: Creating Large Training Sets, Quickly Details |
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Human Decision-Making under Limited Time Pedro A. Ortega, Alan A. Stocker |
Human Decision-Making under Limited Time Details |
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A Communication-Efficient Parallel Algorithm for Decision Tree Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhiming Ma, Tie-Yan Liu |
A Communication-Efficient Parallel Algorithm for Decision Tree Details |
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Understanding the Effective Receptive Field in Deep Convolutional Neural Networks Wenjie Luo, Yujia Li, Raquel Urtasun, Richard S. Zemel |
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks Details |
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Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks Tianfan Xue, Jiajun Wu, Katherine L. Bouman, Bill Freeman |
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks Details |
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Visual Question Answering with Question Representation Update (QRU) Ruiyu Li, Jiaya Jia |
Visual Question Answering with Question Representation Update (QRU) Details |
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Dual Space Gradient Descent for Online Learning Trung Le, Tu Dinh Nguyen, Vu Nguyen, Dinh Q. Phung |
Dual Space Gradient Descent for Online Learning Details |
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Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré |
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much Details |
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Multistage Campaigning in Social Networks Mehrdad Farajtabar, Xiaojing Ye, Sahar Harati, Le Song, Hongyuan Zha |
Multistage Campaigning in Social Networks Details |
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Constraints Based Convex Belief Propagation Yaniv Tenzer, Alexander G. Schwing, Kevin Gimpel, Tamir Hazan |
Constraints Based Convex Belief Propagation Details |
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Dense Associative Memory for Pattern Recognition Dmitry Krotov, John J. Hopfield |
Dense Associative Memory for Pattern Recognition Details |
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Neural Universal Discrete Denoiser Taesup Moon, Seonwoo Min, Byunghan Lee, Sungroh Yoon |
Neural Universal Discrete Denoiser Details |
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Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian Victor Picheny, Robert B. Gramacy, Stefan M. Wild, Sébastien Le Digabel |
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian Details |
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An equivalence between high dimensional Bayes optimal inference and M-estimation Madhu Advani, Surya Ganguli |
An equivalence between high dimensional Bayes optimal inference and M-estimation Details |
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Reward Augmented Maximum Likelihood for Neural Structured Prediction Mohammad Norouzi, Samy Bengio, Zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans |
Reward Augmented Maximum Likelihood for Neural Structured Prediction Details |
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Efficient state-space modularization for planning: theory, behavioral and neural signatures Daniel McNamee, Daniel M. Wolpert, Máté Lengyel |
Efficient state-space modularization for planning: theory, behavioral and neural signatures Details |
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Universal Correspondence Network Christopher B. Choy, JunYoung Gwak, Silvio Savarese, Manmohan Krishna Chandraker |
Universal Correspondence Network Details |
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Felix X. Yu, Ananda Theertha Suresh, Krzysztof Marcin Choromanski, Daniel N. Holtmann-Rice, Sanjiv Kumar |
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Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks Noah J. Apthorpe, Alexander J. Riordan, Rob E. Aguilar, Jan Homann, Yi Gu, David W. Tank, H. Sebastian Seung |
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Swapout: Learning an ensemble of deep architectures Saurabh Singh, Derek Hoiem, David A. Forsyth |
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Active Learning from Imperfect Labelers Songbai Yan, Kamalika Chaudhuri, Tara Javidi |
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Learning What and Where to Draw Scott E. Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee |
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Hierarchical Question-Image Co-Attention for Visual Question Answering Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh |
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Learning Infinite RBMs with Frank-Wolfe Wei Ping, Qiang Liu, Alexander T. Ihler |
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Eli Gutin, Vivek F. Farias |
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Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis Yoshinobu Kawahara |
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Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products Sanghamitra Dutta, Viveck R. Cadambe, Pulkit Grover |
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On Valid Optimal Assignment Kernels and Applications to Graph Classification Nils M. Kriege, Pierre-Louis Giscard, Richard C. Wilson |
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Xiao-Jiao Mao, Chunhua Shen, Yu-Bin Yang |
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Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning Jean-Bastien Grill, Michal Valko, Rémi Munos |
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Tractable Operations for Arithmetic Circuits of Probabilistic Models Yujia Shen, Arthur Choi, Adnan Darwiche |
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Learning Deep Parsimonious Representations Renjie Liao, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun |
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A Probabilistic Programming Approach To Probabilistic Data Analysis Feras Saad, Vikash K. Mansinghka |
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Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA Aapo Hyvärinen, Hiroshi Morioka |
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Feature selection in functional data classification with recursive maxima hunting José L. Torrecilla, Alberto Suárez |
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A state-space model of cross-region dynamic connectivity in MEG/EEG Ying Yang, Elissa Aminoff, Michael J. Tarr, Robert E. Kass |
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"Congruent" and "Opposite" Neurons: Sisters for Multisensory Integration and Segregation Wenhao Zhang, He Wang, K. Y. Michael Wong, Si Wu |
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DISCO Nets : DISsimilarity COefficients Networks Diane Bouchacourt, Pawan Kumar Mudigonda, Sebastian Nowozin |
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Efficient Nonparametric Smoothness Estimation Shashank Singh, Simon S. Du, Barnabás Póczos |
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Coupled Generative Adversarial Networks Ming-Yu Liu, Oncel Tuzel |
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Graph Clustering: Block-models and model free results Yali Wan, Marina Meila |
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Without-Replacement Sampling for Stochastic Gradient Methods Ohad Shamir |
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A Locally Adaptive Normal Distribution Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg |
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A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing Ming Lin, Jieping Ye |
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Interaction Networks for Learning about Objects, Relations and Physics Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, Koray Kavukcuoglu |
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A Bandit Framework for Strategic Regression Yang Liu, Yiling Chen |
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Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions Yichen Wang, Nan Du, Rakshit Trivedi, Le Song |
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Learning to Communicate with Deep Multi-Agent Reinforcement Learning Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson |
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End-to-End Goal-Driven Web Navigation Rodrigo Nogueira, Kyunghyun Cho |
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Efficient Second Order Online Learning by Sketching Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford |
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Threshold Learning for Optimal Decision Making Nathan F. Lepora |
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Learning Multiagent Communication with Backpropagation Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus |
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Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Steven Z. Wu |
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Learning to learn by gradient descent by gradient descent Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas |
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Review Networks for Caption Generation Zhilin Yang, Ye Yuan, Yuexin Wu, William W. Cohen, Ruslan Salakhutdinov |
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Active Learning with Oracle Epiphany Tzu-Kuo Huang, Lihong Li, Ara Vartanian, Saleema Amershi, Xiaojin Zhu |
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Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel |
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Optimal Black-Box Reductions Between Optimization Objectives Zeyuan Allen Zhu, Elad Hazan |
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Fairness in Learning: Classic and Contextual Bandits Matthew Joseph, Michael J. Kearns, Jamie H. Morgenstern, Aaron Roth |
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Variational Information Maximization for Feature Selection Shuyang Gao, Greg Ver Steeg, Aram Galstyan |
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Optimizing affinity-based binary hashing using auxiliary coordinates Ramin Raziperchikolaei, Miguel Á. Carreira-Perpiñán |
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k*-Nearest Neighbors: From Global to Local Oren Anava, Kfir Y. Levy |
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Synthesis of MCMC and Belief Propagation Sungsoo Ahn, Michael Chertkov, Jinwoo Shin |
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Consistent Kernel Mean Estimation for Functions of Random Variables Carl-Johann Simon-Gabriel, Adam Scibior, Ilya O. Tolstikhin, Bernhard Schölkopf |
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Budgeted stream-based active learning via adaptive submodular maximization Kaito Fujii, Hisashi Kashima |
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Qi Lei, Kai Zhong, Inderjit S. Dhillon |
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Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani |
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Launch and Iterate: Reducing Prediction Churn Mahdi Milani Fard, Quentin Cormier, Kevin Robert Canini, Maya R. Gupta |
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Dan Garber, Ofer Meshi |
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Disentangling factors of variation in deep representation using adversarial training Michaël Mathieu, Junbo Jake Zhao, Pablo Sprechmann, Aditya Ramesh, Yann LeCun |
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R-FCN: Object Detection via Region-based Fully Convolutional Networks Jifeng Dai, Yi Li, Kaiming He, Jian Sun |
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Fast recovery from a union of subspaces Chinmay Hegde, Piotr Indyk, Ludwig Schmidt |
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Verification Based Solution for Structured MAB Problems Zohar S. Karnin |
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Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators Shashank Singh, Barnabás Póczos |
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Learned Region Sparsity and Diversity Also Predicts Visual Attention Zijun Wei, Hossein Adeli, Minh Hoai, Gregory J. Zelinsky, Dimitris Samaras |
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Community Detection on Evolving Graphs Aris Anagnostopoulos, Jakub Lacki, Silvio Lattanzi, Stefano Leonardi, Mohammad Mahdian |
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Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation Jianxu Chen, Lin Yang, Yizhe Zhang, Mark S. Alber, Danny Ziyi Chen |
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Interpretable Distribution Features with Maximum Testing Power Wittawat Jitkrittum, Zoltán Szabó, Kacper P. Chwialkowski, Arthur Gretton |
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Observational-Interventional Priors for Dose-Response Learning Ricardo Silva |
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Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction Hsiang-Fu Yu, Nikhil Rao, Inderjit S. Dhillon |
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Bayesian Intermittent Demand Forecasting for Large Inventories Matthias W. Seeger, David Salinas, Valentin Flunkert |
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Deep Exploration via Bootstrapped DQN Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy |
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Rudy Bunel, Alban Desmaison, Pawan Kumar Mudigonda, Pushmeet Kohli, Philip H. S. Torr |
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Dale Schuurmans, Martin Zinkevich |
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Structured Matrix Recovery via the Generalized Dantzig Selector Sheng Chen, Arindam Banerjee |
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Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction Jacob Steinhardt, Gregory Valiant, Moses Charikar |
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FPNN: Field Probing Neural Networks for 3D Data Yangyan Li, Sören Pirk, Hao Su, Charles Ruizhongtai Qi, Leonidas J. Guibas |
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Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola |
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Iterative Refinement of the Approximate Posterior for Directed Belief Networks R. Devon Hjelm, Ruslan Salakhutdinov, Kyunghyun Cho, Nebojsa Jojic, Vince D. Calhoun, Junyoung Chung |
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Mistake Bounds for Binary Matrix Completion Mark Herbster, Stephen Pasteris, Massimiliano Pontil |
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Contextual semibandits via supervised learning oracles Akshay Krishnamurthy, Alekh Agarwal, Miroslav Dudík |
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Kernel Bayesian Inference with Posterior Regularization Yang Song, Jun Zhu, Yong Ren |
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Learning Sparse Gaussian Graphical Models with Overlapping Blocks Mohammad Javad Hosseini, Su-In Lee |
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Mixed Linear Regression with Multiple Components Kai Zhong, Prateek Jain, Inderjit S. Dhillon |
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Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition Seyed Hamidreza Kasaei, Ana Maria Tomé, Luís Seabra Lopes |
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Variational Bayes on Monte Carlo Steroids Aditya Grover, Stefano Ermon |
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Dimension-Free Iteration Complexity of Finite Sum Optimization Problems Yossi Arjevani, Ohad Shamir |
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Preference Completion from Partial Rankings Suriya Gunasekar, Oluwasanmi Koyejo, Joydeep Ghosh |
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Generating Images with Perceptual Similarity Metrics based on Deep Networks Alexey Dosovitskiy, Thomas Brox |
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Mapping Estimation for Discrete Optimal Transport Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard |
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Learning shape correspondence with anisotropic convolutional neural networks Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein |
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Variational Inference in Mixed Probabilistic Submodular Models Josip Djolonga, Sebastian Tschiatschek, Andreas Krause |
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Unsupervised Learning of 3D Structure from Images Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter W. Battaglia, Max Jaderberg, Nicolas Heess |
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Examples are not enough, learn to criticize! Criticism for Interpretability Been Kim, Oluwasanmi Koyejo, Rajiv Khanna |
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Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease Hao Henry Zhou, Vamsi K. Ithapu, Sathya Narayanan Ravi, Vikas Singh, Grace Wahba, Sterling C. Johnson |
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Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach Rémi Lam, Karen Willcox, David H. Wolpert |
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GAP Safe Screening Rules for Sparse-Group Lasso Eugène Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon |
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Xinyang Yi, Zhaoran Wang, Zhuoran Yang, Constantine Caramanis, Han Liu |
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Exact Recovery of Hard Thresholding Pursuit Xiao-Tong Yuan, Ping Li, Tong Zhang |
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Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics Wei-Shou Hsu, Pascal Poupart |
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Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables Mauro Scanagatta, Giorgio Corani, Cassio Polpo de Campos, Marco Zaffalon |
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Kieran Milan, Joel Veness, James Kirkpatrick, Michael H. Bowling, Anna Koop, Demis Hassabis |
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Differential Privacy without Sensitivity Kentaro Minami, Hiromi Arai, Issei Sato, Hiroshi Nakagawa |
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Structured Prediction Theory Based on Factor Graph Complexity Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang |
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Tagger: Deep Unsupervised Perceptual Grouping Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Harri Valpola, Jürgen Schmidhuber |
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Eliciting Categorical Data for Optimal Aggregation Chien-Ju Ho, Rafael M. Frongillo, Yiling Chen |
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Thomas Laurent, James H. von Brecht, Xavier Bresson, Arthur Szlam |
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Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári |
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Sparse Support Recovery with Non-smooth Loss Functions Kévin Degraux, Gabriel Peyré, Jalal Fadili, Laurent Jacques |
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A Simple Practical Accelerated Method for Finite Sums Aaron Defazio |
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Near-Optimal Smoothing of Structured Conditional Probability Matrices Moein Falahatgar, Mesrob I. Ohannessian, Alon Orlitsky |
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Active Nearest-Neighbor Learning in Metric Spaces Aryeh Kontorovich, Sivan Sabato, Ruth Urner |
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Conditional Image Generation with PixelCNN Decoders Aäron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Koray Kavukcuoglu, Oriol Vinyals, Alex Graves |
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Clustering with Same-Cluster Queries Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David |
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Pruning Random Forests for Prediction on a Budget Feng Nan, Joseph Wang, Venkatesh Saligrama |
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Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes Chris Junchi Li, Zhaoran Wang, Han Liu |
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Multimodal Residual Learning for Visual QA Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak, Min-Oh Heo, Jeonghee Kim, JungWoo Ha, Byoung-Tak Zhang |
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Lifelong Learning with Weighted Majority Votes Anastasia Pentina, Ruth Urner |
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RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, Walter F. Stewart |
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New Liftable Classes for First-Order Probabilistic Inference Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck, David Poole |
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Convergence guarantees for kernel-based quadrature rules in misspecified settings Motonobu Kanagawa, Bharath K. Sriperumbudur, Kenji Fukumizu |
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Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon) Yi Xu, Yan Yan, Qihang Lin, Tianbao Yang |
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Matching Networks for One Shot Learning Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, Daan Wierstra |
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Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing Farshad Lahouti, Babak Hassibi |
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Guided Policy Search via Approximate Mirror Descent William H. Montgomery, Sergey Levine |
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A posteriori error bounds for joint matrix decomposition problems Nicolò Colombo, Nikos Vlassis |
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Optimal Tagging with Markov Chain Optimization Nir Rosenfeld, Amir Globerson |
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Estimating the Size of a Large Network and its Communities from a Random Sample Lin Chen, Amin Karbasi, Forrest W. Crawford |
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A Sparse Interactive Model for Matrix Completion with Side Information Jin Lu, Guannan Liang, Jiangwen Sun, Jinbo Bi |
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Assortment Optimization Under the Mallows model Antoine Désir, Vineet Goyal, Srikanth Jagabathula, Danny Segev |
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Optimal Cluster Recovery in the Labeled Stochastic Block Model Se-Young Yun, Alexandre Proutière |
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Wasserstein Training of Restricted Boltzmann Machines Grégoire Montavon, Klaus-Robert Müller, Marco Cuturi |
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On Robustness of Kernel Clustering Bowei Yan, Purnamrita Sarkar |
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Dino Oglic, Thomas Gärtner |
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Maximization of Approximately Submodular Functions Thibaut Horel, Yaron Singer |
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Nearly Isometric Embedding by Relaxation James McQueen, Marina Meila, Dominique Joncas |
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A Non-generative Framework and Convex Relaxations for Unsupervised Learning Elad Hazan, Tengyu Ma |
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The Limits of Learning with Missing Data Brian Bullins, Elad Hazan, Tomer Koren |
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Bayesian latent structure discovery from multi-neuron recordings Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow |
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Fast and Provably Good Seedings for k-Means Olivier Bachem, Mario Lucic, Seyed Hamed Hassani, Andreas Krause |
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Bayesian Optimization for Probabilistic Programs Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood |
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Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering Dogyoon Song, Christina E. Lee, Yihua Li, Devavrat Shah |
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Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated Namrata Vaswani, Han Guo |
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Coin Betting and Parameter-Free Online Learning Francesco Orabona, Dávid Pál |
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Graphons, mergeons, and so on! Justin Eldridge, Mikhail Belkin, Yusu Wang |
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Generating Long-term Trajectories Using Deep Hierarchical Networks Stephan Zheng, Yisong Yue, Jennifer Hobbs |
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Exponential expressivity in deep neural networks through transient chaos Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, Surya Ganguli |
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NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization Davood Hajinezhad, Mingyi Hong, Tuo Zhao, Zhaoran Wang |
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Bi-Objective Online Matching and Submodular Allocations Hossein Esfandiari, Nitish Korula, Vahab S. Mirrokni |
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Peter Schulam, Raman Arora |
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Satisfying Real-world Goals with Dataset Constraints Gabriel Goh, Andrew Cotter, Maya R. Gupta, Michael P. Friedlander |
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Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats Bipin Rajendran, Pulkit Tandon, Yash H. Malviya |
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Deep Learning Models of the Retinal Response to Natural Scenes Lane McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen Baccus |
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Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles Stefan Lee, Senthil Purushwalkam, Michael Cogswell, Viresh Ranjan, David J. Crandall, Dhruv Batra |
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Online and Differentially-Private Tensor Decomposition Yining Wang, Anima Anandkumar |
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Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation Tejas D. Kulkarni, Karthik Narasimhan, Ardavan Saeedi, Josh Tenenbaum |
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Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages Yin Cheng Ng, Pawel M. Chilinski, Ricardo Silva |
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Xiangru Lian, Huan Zhang, Cho-Jui Hsieh, Yijun Huang, Ji Liu |
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Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nati Srebro |
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The Robustness of Estimator Composition Pingfan Tang, Jeff M. Phillips |
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Convex Two-Layer Modeling with Latent Structure Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen |
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Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama |
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Learning Kernels with Random Features Aman Sinha, John C. Duchi |
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Attend, Infer, Repeat: Fast Scene Understanding with Generative Models S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton |
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Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks Tim Salimans, Diederik P. Kingma |
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Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity Eugene Belilovsky, Gaël Varoquaux, Matthew B. Blaschko |
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Aviv Tamar, Sergey Levine, Pieter Abbeel, Yi Wu, Garrett Thomas |
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Identification and Overidentification of Linear Structural Equation Models Bryant Chen |
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Proximal Deep Structured Models Shenlong Wang, Sanja Fidler, Raquel Urtasun |
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Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics Travis Monk, Cristina Savin, Jörg Lücke |
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Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning Taiji Suzuki, Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami |
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Probabilistic Inference with Generating Functions for Poisson Latent Variable Models Kevin Winner, Daniel R. Sheldon |
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Learning under uncertainty: a comparison between R-W and Bayesian approach He Huang, Martin P. Paulus |
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Regularized Nonlinear Acceleration Damien Scieur, Alexandre d'Aspremont, Francis R. Bach |
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Learning the Number of Neurons in Deep Networks Jose M. Alvarez, Mathieu Salzmann |
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Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random Ilya Shpitser |
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Unsupervised Learning for Physical Interaction through Video Prediction Chelsea Finn, Ian J. Goodfellow, Sergey Levine |
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Scalable Adaptive Stochastic Optimization Using Random Projections Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen |
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Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen |
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Integrated perception with recurrent multi-task neural networks Hakan Bilen, Andrea Vedaldi |
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Learning Structured Sparsity in Deep Neural Networks Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li |
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A Probabilistic Model of Social Decision Making based on Reward Maximization Koosha Khalvati, Seongmin A. Park, Jean-Claude Dreher, Rajesh P. Rao |
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Natural-Parameter Networks: A Class of Probabilistic Neural Networks Hao Wang, Xingjian Shi, Dit-Yan Yeung |
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Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates Yuanzhi Li, Yingyu Liang, Andrej Risteski |
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Reconstructing Parameters of Spreading Models from Partial Observations Andrey Y. Lokhov |
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Batched Gaussian Process Bandit Optimization via Determinantal Point Processes Tarun Kathuria, Amit Deshpande, Pushmeet Kohli |
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Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games Sougata Chaudhuri, Ambuj Tewari |
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Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution Christopher Lynn, Daniel D. Lee |
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Rényi Divergence Variational Inference Yingzhen Li, Richard E. Turner |
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Robustness of classifiers: from adversarial to random noise Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard |
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Learning Sensor Multiplexing Design through Back-propagation Ayan Chakrabarti |
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A forward model at Purkinje cell synapses facilitates cerebellar anticipatory control Ivan Herreros, Xerxes D. Arsiwalla, Paul F. M. J. Verschure |
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On Explore-Then-Commit strategies Aurélien Garivier, Tor Lattimore, Emilie Kaufmann |
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Fast Active Set Methods for Online Spike Inference from Calcium Imaging Johannes Friedrich, Liam Paninski |
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Bayesian optimization for automated model selection Gustavo Malkomes, Chip Schaff, Roman Garnett |
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Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P. Xing |
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Parameter Learning for Log-supermodular Distributions Tatiana Shpakova, Francis R. Bach |
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Generative Adversarial Imitation Learning Jonathan Ho, Stefano Ermon |
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Inference by Reparameterization in Neural Population Codes Rajkumar Vasudeva Raju, Xaq Pitkow |
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Robust Spectral Detection of Global Structures in the Data by Learning a Regularization Pan Zhang |
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Improving PAC Exploration Using the Median Of Means Jason Pazis, Ronald Parr, Jonathan P. How |
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A Pseudo-Bayesian Algorithm for Robust PCA Tae-Hyun Oh, Yasuyuki Matsushita, In-So Kweon, David P. Wipf |
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Estimating the class prior and posterior from noisy positives and unlabeled data Shantanu Jain, Martha White, Predrag Radivojac |
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Sara Magliacane, Tom Claassen, Joris M. Mooij |
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DeepMath - Deep Sequence Models for Premise Selection Geoffrey Irving, Christian Szegedy, Alexander A. Alemi, Niklas Eén, François Chollet, Josef Urban |
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Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making Himabindu Lakkaraju, Jure Leskovec |
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Simple and Efficient Weighted Minwise Hashing Anshumali Shrivastava |
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CRF-CNN: Modeling Structured Information in Human Pose Estimation Xiao Chu, Wanli Ouyang, Hongsheng Li, Xiaogang Wang |
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Finite Sample Prediction and Recovery Bounds for Ordinal Embedding Lalit Jain, Kevin G. Jamieson, Robert D. Nowak |
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Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions Siddartha Y. Ramamohan, Arun Rajkumar, Shivani Agarwal |
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CliqueCNN: Deep Unsupervised Exemplar Learning Miguel Ángel Bautista, Artsiom Sanakoyeu, Ekaterina Tikhoncheva, Björn Ommer |
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Linear Relaxations for Finding Diverse Elements in Metric Spaces Aditya Bhaskara, Mehrdad Ghadiri, Vahab S. Mirrokni, Ola Svensson |
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SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling Dehua Cheng, Richard Peng, Yan Liu, Ioakeim Perros |
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Strategic Attentive Writer for Learning Macro-Actions Alexander Vezhnevets, Volodymyr Mnih, Simon Osindero, Alex Graves, Oriol Vinyals, John Agapiou, Koray Kavukcuoglu |
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Average-case hardness of RIP certification Tengyao Wang, Quentin Berthet, Yaniv Plan |
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks Anh Mai Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune |
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Regret Bounds for Non-decomposable Metrics with Missing Labels Nagarajan Natarajan, Prateek Jain |
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Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares Rong Zhu |
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Deep Submodular Functions: Definitions and Learning Brian W. Dolhansky, Jeff A. Bilmes |
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Threshold Bandits, With and Without Censored Feedback Jacob D. Abernethy, Kareem Amin, Ruihao Zhu |
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Asynchronous Parallel Greedy Coordinate Descent Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, James Demmel, Cho-Jui Hsieh |
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Dual Learning for Machine Translation Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma |
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An ensemble diversity approach to supervised binary hashing Miguel Á. Carreira-Perpiñán, Ramin Raziperchikolaei |
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Maja R. Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei |
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Cooperative Inverse Reinforcement Learning Dylan Hadfield-Menell, Stuart J. Russell, Pieter Abbeel, Anca D. Dragan |
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Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S. Talwalkar |
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Refined Lower Bounds for Adversarial Bandits Sébastien Gerchinovitz, Tor Lattimore |
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Large Margin Discriminant Dimensionality Reduction in Prediction Space Mohammad J. Saberian, Jose Costa Pereira, Nuno Vasconcelos, Can Xu |
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The Power of Adaptivity in Identifying Statistical Alternatives Kevin G. Jamieson, Daniel Haas, Benjamin Recht |
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Unsupervised Risk Estimation Using Only Conditional Independence Structure Jacob Steinhardt, Percy Liang |
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f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization Sebastian Nowozin, Botond Cseke, Ryota Tomioka |
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Sample Complexity of Automated Mechanism Design Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik |
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Communication-Optimal Distributed Clustering Jiecao Chen, He Sun, David P. Woodruff, Qin Zhang |
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CMA-ES with Optimal Covariance Update and Storage Complexity Oswin Krause, Dídac Rodríguez Arbonès, Christian Igel |
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Local Similarity-Aware Deep Feature Embedding Chen Huang, Chen Change Loy, Xiaoou Tang |
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Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks Hao Wang, Xingjian Shi, Dit-Yan Yeung |
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The Multi-fidelity Multi-armed Bandit Kirthevasan Kandasamy, Gautam Dasarathy, Barnabás Póczos, Jeff G. Schneider |
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Supervised Learning with Tensor Networks Edwin Miles Stoudenmire, David J. Schwab |
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Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, Honglak Lee |
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Measuring Neural Net Robustness with Constraints Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya V. Nori, Antonio Criminisi |
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Global Analysis of Expectation Maximization for Mixtures of Two Gaussians Ji Xu, Daniel J. Hsu, Arian Maleki |
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Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models Juho Lee, Lancelot F. James, Seungjin Choi |
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Search Improves Label for Active Learning Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang |
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Professor Forcing: A New Algorithm for Training Recurrent Networks Anirudh Goyal, Alex Lamb, Ying Zhang, Saizheng Zhang, Aaron C. Courville, Yoshua Bengio |
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High Dimensional Structured Superposition Models Qilong Gu, Arindam Banerjee |
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The Multiple Quantile Graphical Model Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani |
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Learning values across many orders of magnitude Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver |
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Multi-armed Bandits: Competing with Optimal Sequences Zohar S. Karnin, Oren Anava |
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Gaussian Processes for Survival Analysis Tamara Fernandez, Nicolas Rivera, Yee Whye Teh |
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Reshaped Wirtinger Flow for Solving Quadratic System of Equations Huishuai Zhang, Yingbin Liang |
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Spectral Learning of Dynamic Systems from Nonequilibrium Data Hao Wu, Frank Noé |
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Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation George Papamakarios, Iain Murray |
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Using Fast Weights to Attend to the Recent Past Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu |
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Barzilai-Borwein Step Size for Stochastic Gradient Descent Conghui Tan, Shiqian Ma, Yu-Hong Dai, Yuqiu Qian |
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Improved Dropout for Shallow and Deep Learning Zhe Li, Boqing Gong, Tianbao Yang |
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Stochastic Variational Deep Kernel Learning Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing |
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Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël Richard |
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A Bio-inspired Redundant Sensing Architecture Anh Tuan Nguyen, Jian Xu, Zhi Yang |
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Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, Adam Tauman Kalai |
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Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition Théodore Bluche |
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beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data Valentina Zantedeschi, Rémi Emonet, Marc Sebban |
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Deep Learning for Predicting Human Strategic Behavior Jason S. Hartford, James R. Wright, Kevin Leyton-Brown |
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SURGE: Surface Regularized Geometry Estimation from a Single Image Peng Wang, Xiaohui Shen, Bryan C. Russell, Scott Cohen, Brian L. Price, Alan L. Yuille |
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Robust k-means: a Theoretical Revisit Alexandros Georgogiannis |
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Stochastic Gradient Geodesic MCMC Methods Chang Liu, Jun Zhu, Yang Song |
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Doubly Convolutional Neural Networks Shuangfei Zhai, Yu Cheng, Zhongfei (Mark) Zhang, Weining Lu |
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Generating Videos with Scene Dynamics Carl Vondrick, Hamed Pirsiavash, Antonio Torralba |
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Minimizing Quadratic Functions in Constant Time Kohei Hayashi, Yuichi Yoshida |
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Unifying Count-Based Exploration and Intrinsic Motivation Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos |
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Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep Ravikumar, Inderjit S. Dhillon |
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Causal Bandits: Learning Good Interventions via Causal Inference Finnian Lattimore, Tor Lattimore, Mark D. Reid |
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Learning and Forecasting Opinion Dynamics in Social Networks Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, Manuel Gomez-Rodriguez |
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Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling Chengtao Li, Suvrit Sra, Stefanie Jegelka |
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MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild Grégory Rogez, Cordelia Schmid |
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Latent Attention For If-Then Program Synthesis Chang Liu, Xinyun Chen, Eui Chul Richard Shin, Mingcheng Chen, Dawn Xiaodong Song |
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Learning Parametric Sparse Models for Image Super-Resolution Yongbo Li, Weisheng Dong, Xuemei Xie, Guangming Shi, Xin Li, Donglai Xu |
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Adaptive optimal training of animal behavior Ji Hyun Bak, Jung Choi, Ilana Witten, Athena Akrami, Jonathan W. Pillow |
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A Theoretically Grounded Application of Dropout in Recurrent Neural Networks Yarin Gal, Zoubin Ghahramani |
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Leveraging Sparsity for Efficient Submodular Data Summarization Erik M. Lindgren, Shanshan Wu, Alexandros G. Dimakis |
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High-Rank Matrix Completion and Clustering under Self-Expressive Models Ehsan Elhamifar |
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General Tensor Spectral Co-clustering for Higher-Order Data Tao Wu, Austin R. Benson, David F. Gleich |
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An algorithm for L1 nearest neighbor search via monotonic embedding Xinan Wang, Sanjoy Dasgupta |
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Tracking the Best Expert in Non-stationary Stochastic Environments Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu |
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Achieving budget-optimality with adaptive schemes in crowdsourcing Ashish Khetan, Sewoong Oh |
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How Deep is the Feature Analysis underlying Rapid Visual Categorization? Sven Eberhardt, Jonah G. Cader, Thomas Serre |
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Stochastic Three-Composite Convex Minimization Alp Yurtsever, Bang Công Vu, Volkan Cevher |
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Optimal spectral transportation with application to music transcription Rémi Flamary, Cédric Févotte, Nicolas Courty, Valentin Emiya |
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Safe Policy Improvement by Minimizing Robust Baseline Regret Mohammad Ghavamzadeh, Marek Petrik, Yinlam Chow |
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Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games Maximilian Balandat, Walid Krichene, Claire Tomlin, Alexandre M. Bayen |
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Scaled Least Squares Estimator for GLMs in Large-Scale Problems Murat A. Erdogdu, Lee H. Dicker, Mohsen Bayati |
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Emmanuel Abbe, Colin Sandon |
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What Makes Objects Similar: A Unified Multi-Metric Learning Approach Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang, Zhi-Hua Zhou |
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Selective inference for group-sparse linear models Fan Yang, Rina Foygel Barber, Prateek Jain, John D. Lafferty |
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Linear dynamical neural population models through nonlinear embeddings Yuanjun Gao, Evan W. Archer, Liam Paninski, John P. Cunningham |
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Fast and accurate spike sorting of high-channel count probes with KiloSort Marius Pachitariu, Nicholas A. Steinmetz, Shabnam N. Kadir, Matteo Carandini, Kenneth D. Harris |
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Improved Deep Metric Learning with Multi-class N-pair Loss Objective Kihyuk Sohn |
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Faster Projection-free Convex Optimization over the Spectrahedron Dan Garber |
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Sequential Neural Models with Stochastic Layers Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther |
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Optimal Learning for Multi-pass Stochastic Gradient Methods Junhong Lin, Lorenzo Rosasco |
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Memory-Efficient Backpropagation Through Time Audrunas Gruslys, Rémi Munos, Ivo Danihelka, Marc Lanctot, Alex Graves |
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Higher-Order Factorization Machines Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata |
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A Unified Approach for Learning the Parameters of Sum-Product Networks Han Zhao, Pascal Poupart, Geoffrey J. Gordon |
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Structure-Blind Signal Recovery Dmitry Ostrovsky, Zaïd Harchaoui, Anatoli B. Juditsky, Arkadi Nemirovski |
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Structured Sparse Regression via Greedy Hard Thresholding Prateek Jain, Nikhil Rao, Inderjit S. Dhillon |
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Conditional Generative Moment-Matching Networks Yong Ren, Jun Zhu, Jialian Li, Yucen Luo |
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Causal meets Submodular: Subset Selection with Directed Information Yuxun Zhou, Costas J. Spanos |
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A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++ Dennis Wei |
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Distributed Flexible Nonlinear Tensor Factorization Shandian Zhe, Kai Zhang, Pengyuan Wang, Kuang-chih Lee, Zenglin Xu, Yuan Qi, Zoubin Ghahramani |
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Towards Conceptual Compression Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra |
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Equality of Opportunity in Supervised Learning Moritz Hardt, Eric Price, Nati Srebro |
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Measuring the reliability of MCMC inference with bidirectional Monte Carlo Roger B. Grosse, Siddharth Ancha, Daniel M. Roy |
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Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent Chi Jin, Sham M. Kakade, Praneeth Netrapalli |
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An Architecture for Deep, Hierarchical Generative Models Philip Bachman |
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CNNpack: Packing Convolutional Neural Networks in the Frequency Domain Yunhe Wang, Chang Xu, Shan You, Dacheng Tao, Chao Xu |
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Algorithms and matching lower bounds for approximately-convex optimization Andrej Risteski, Yuanzhi Li |
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Statistical Inference for Cluster Trees Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry A. Wasserman |
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Incremental Variational Sparse Gaussian Process Regression Ching-An Cheng, Byron Boots |
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Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation Weihao Gao, Sewoong Oh, Pramod Viswanath |
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Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst |
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Adaptive Averaging in Accelerated Descent Dynamics Walid Krichene, Alexandre M. Bayen, Peter L. Bartlett |
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Stochastic Gradient MCMC with Stale Gradients Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin |
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James Newling, François Fleuret |
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Optimal Architectures in a Solvable Model of Deep Networks Jonathan Kadmon, Haim Sompolinsky |
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Learning Deep Embeddings with Histogram Loss Evgeniya Ustinova, Victor S. Lempitsky |
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Kronecker Determinantal Point Processes Zelda E. Mariet, Suvrit Sra |
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Fast Distributed Submodular Cover: Public-Private Data Summarization Baharan Mirzasoleiman, Morteza Zadimoghaddam, Amin Karbasi |
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Linear Feature Encoding for Reinforcement Learning Zhao Song, Ronald E. Parr, Xuejun Liao, Lawrence Carin |
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Deep Neural Networks with Inexact Matching for Person Re-Identification Arulkumar Subramaniam, Moitreya Chatterjee, Anurag Mittal |
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Semiparametric Differential Graph Models Pan Xu, Quanquan Gu |
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Towards Unifying Hamiltonian Monte Carlo and Slice Sampling Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin |
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Hardness of Online Sleeping Combinatorial Optimization Problems Satyen Kale, Chansoo Lee, Dávid Pál |
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Automated scalable segmentation of neurons from multispectral images Uygar Sümbül, Douglas H. Roossien, Dawen Cai, Fei Chen, Nicholas Barry, John P. Cunningham, Edward S. Boyden, Liam Paninski |
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Object based Scene Representations using Fisher Scores of Local Subspace Projections Mandar Dixit, Nuno Vasconcelos |
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Bayesian Optimization with Robust Bayesian Neural Networks Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, Frank Hutter |
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Tight Complexity Bounds for Optimizing Composite Objectives Blake E. Woodworth, Nati Srebro |
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Solving Marginal MAP Problems with NP Oracles and Parity Constraints Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, Bart Selman |
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Sub-sampled Newton Methods with Non-uniform Sampling Peng Xu, Jiyan Yang, Farbod Roosta-Khorasani, Christopher Ré, Michael W. Mahoney |
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Ladder Variational Autoencoders Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, Ole Winther |
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Geometric Dirichlet Means Algorithm for topic inference Mikhail Yurochkin, XuanLong Nguyen |
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Optimal Binary Classifier Aggregation for General Losses Akshay Balsubramani, Yoav Freund |
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Low-Rank Regression with Tensor Responses Guillaume Rabusseau, Hachem Kadri |
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Deconvolving Feedback Loops in Recommender Systems Ayan Sinha, David F. Gleich, Karthik Ramani |
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A Powerful Generative Model Using Random Weights for the Deep Image Representation Kun He, Yan Wang, John E. Hopcroft |
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SDP Relaxation with Randomized Rounding for Energy Disaggregation Kiarash Shaloudegi, András György, Csaba Szepesvári, Wilsun Xu |
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Joint quantile regression in vector-valued RKHSs Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc |
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Understanding Probabilistic Sparse Gaussian Process Approximations Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen |
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Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes Hassan A. Kingravi, Harshal R. Maske, Girish Chowdhary |
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The Generalized Reparameterization Gradient Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei |
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Fast Algorithms for Robust PCA via Gradient Descent Xinyang Yi, Dohyung Park, Yudong Chen, Constantine Caramanis |
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On the Recursive Teaching Dimension of VC Classes Xi Chen, Yu Cheng, Bo Tang |
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Can Active Memory Replace Attention? Lukasz Kaiser, Samy Bengio |
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Stochastic Variance Reduction Methods for Saddle-Point Problems Balamurugan Palaniappan, Francis R. Bach |
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Computational and Statistical Tradeoffs in Learning to Rank Ashish Khetan, Sewoong Oh |
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On Multiplicative Integration with Recurrent Neural Networks Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov |
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Clustering with Bregman Divergences: an Asymptotic Analysis Chaoyue Liu, Mikhail Belkin |
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Learning Bound for Parameter Transfer Learning Wataru Kumagai |
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SoundNet: Learning Sound Representations from Unlabeled Video Yusuf Aytar, Carl Vondrick, Antonio Torralba |
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Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah D. Goodman |
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Improving Variational Autoencoders with Inverse Autoregressive Flow Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, Max Welling |
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Multi-step learning and underlying structure in statistical models Maia Fraser |
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Oleg Grinchuk, Vadim Lebedev, Victor S. Lempitsky |
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Large-Scale Price Optimization via Network Flow Shinji Ito, Ryohei Fujimaki |
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One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities Michalis K. Titsias |
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Stephen Ragain, Johan Ugander |
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Agnostic Estimation for Misspecified Phase Retrieval Models Matey Neykov, Zhaoran Wang, Han Liu |
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Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data Xinghua Lou, Ken Kansky, Wolfgang Lehrach, C. C. Laan, Bhaskara Marthi, D. Scott Phoenix, Dileep George |
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A Probabilistic Framework for Deep Learning Ankit B. Patel, Minh Tan Nguyen, Richard G. Baraniuk |
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Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back Vitaly Feldman |
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Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions Ayan Chakrabarti, Jingyu Shao, Greg Shakhnarovich |
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Blind Attacks on Machine Learners Alex Beatson, Zhaoran Wang, Han Liu |
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Learning Transferrable Representations for Unsupervised Domain Adaptation Ozan Sener, Hyun Oh Song, Ashutosh Saxena, Silvio Savarese |
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SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques Elad Richardson, Rom Herskovitz, Boris Ginsburg, Michael Zibulevsky |
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PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions Mikhail Figurnov, Aizhan Ibraimova, Dmitry P. Vetrov, Pushmeet Kohli |
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Hierarchical Clustering via Spreading Metrics Aurko Roy, Sebastian Pokutta |
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Fast and Flexible Monotonic Functions with Ensembles of Lattices Mahdi Milani Fard, Kevin Robert Canini, Andrew Cotter, Jan Pfeifer, Maya R. Gupta |
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Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization Tyler B. Johnson, Carlos Guestrin |
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PAC Reinforcement Learning with Rich Observations Akshay Krishnamurthy, Alekh Agarwal, John Langford |
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A Minimax Approach to Supervised Learning Farzan Farnia, David Tse |
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Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow Gang Wang, Georgios B. Giannakis |
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel |
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Shreyas Saxena, Jakob Verbeek |
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On Regularizing Rademacher Observation Losses Richard Nock |
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Supervised learning through the lens of compression Ofir David, Shay Moran, Amir Yehudayoff |
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Learning Additive Exponential Family Graphical Models via \ell_{2, 1}-norm Regularized M-Estimation Xiao-Tong Yuan, Ping Li, Tong Zhang, Qingshan Liu, Guangcan Liu |
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Matrix Completion has No Spurious Local Minimum Rong Ge, Jason D. Lee, Tengyu Ma |
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Bootstrap Model Aggregation for Distributed Statistical Learning Jun Han, Qiang Liu |
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Zhen Xu, Wen Dong, Sargur N. Srihari |
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Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, Volkan Cevher |
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Corinna Cortes, Giulia DeSalvo, Mehryar Mohri |
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Umut Güçlü, Jordy Thielen, Michael Hanke, Marcel van Gerven, Marcel A. J. van Gerven |
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Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information Alexander Shishkin, Anastasia A. Bezzubtseva, Alexey Drutsa, Ilia Shishkov, Ekaterina Gladkikh, Gleb Gusev, Pavel Serdyukov |
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Computing and maximizing influence in linear threshold and triggering models Justin T. Khim, Varun Jog, Po-Ling Loh |
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Sorting out typicality with the inverse moment matrix SOS polynomial Edouard Pauwels, Jean B. Lasserre |
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An Online Sequence-to-Sequence Model Using Partial Conditioning Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio |
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Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan |
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Adaptive Concentration Inequalities for Sequential Decision Problems Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon |
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Combinatorial Multi-Armed Bandit with General Reward Functions Wei Chen, Wei Hu, Fu Li, Jian Li, Yu Liu, Pinyan Lu |
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Learning User Perceived Clusters with Feature-Level Supervision Ting-Yu Cheng, Guiguan Lin, Xinyang Gong, Kang-Jun Liu, Shan-Hung Wu |
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Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition Ahmed M. Alaa, Mihaela van der Schaar |
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Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods Cristina Savin, Gasper Tkacik |
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Dynamic Network Surgery for Efficient DNNs Yiwen Guo, Anbang Yao, Yurong Chen |
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Supervised Word Mover's Distance Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger |
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Variational Autoencoder for Deep Learning of Images, Labels and Captions Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, Lawrence Carin |
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Learning Influence Functions from Incomplete Observations Xinran He, Ke Xu, David Kempe, Yan Liu |
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Poisson-Gamma dynamical systems Aaron Schein, Hanna M. Wallach, Mingyuan Zhou |
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Combinatorial Energy Learning for Image Segmentation Jeremy B. Maitin-Shepard, Viren Jain, Michal Januszewski, Peter Li, Pieter Abbeel |
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Combining Low-Density Separators with CNNs Yu-Xiong Wang, Martial Hebert |
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Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio |
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Coresets for Scalable Bayesian Logistic Regression Jonathan H. Huggins, Trevor Campbell, Tamara Broderick |
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Dimensionality Reduction of Massive Sparse Datasets Using Coresets Dan Feldman, Mikhail Volkov, Daniela Rus |
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A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics William Hoiles, Mihaela van der Schaar |
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Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition Jinzhuo Wang, Wenmin Wang, Xiongtao Chen, Ronggang Wang, Wen Gao |
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Single Pass PCA of Matrix Products Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis |
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A Consistent Regularization Approach for Structured Prediction Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi |
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Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments Ransalu Senanayake, Lionel Ott, Simon Timothy O'Callaghan, Fabio Tozeto Ramos |
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Operator Variational Inference Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei |
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A Multi-Batch L-BFGS Method for Machine Learning Albert S. Berahas, Jorge Nocedal, Martin Takác |
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Density Estimation via Discrepancy Based Adaptive Sequential Partition Dangna Li, Kun Yang, Wing Hung Wong |
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Graphical Time Warping for Joint Alignment of Multiple Curves Yizhi Wang, David J. Miller, Kira Poskanzer, Yue Wang, Lin Tian, Guoqiang Yu |
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Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes Jack W. Rae, Jonathan J. Hunt, Ivo Danihelka, Timothy Harley, Andrew W. Senior, Gregory Wayne, Alex Graves, Tim Lillicrap |
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Combinatorial semi-bandit with known covariance Rémy Degenne, Vianney Perchet |
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Safe and Efficient Off-Policy Reinforcement Learning Rémi Munos, Tom Stepleton, Anna Harutyunyan, Marc G. Bellemare |
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Split LBI: An Iterative Regularization Path with Structural Sparsity Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan Yao |
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Improved Techniques for Training GANs Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen |
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Global Optimality of Local Search for Low Rank Matrix Recovery Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro |
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A primal-dual method for conic constrained distributed optimization problems Necdet Serhat Aybat, Erfan Yazdandoost Hamedani |
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Local Minimax Complexity of Stochastic Convex Optimization Sabyasachi Chatterjee, John C. Duchi, John D. Lafferty, Yuancheng Zhu |
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Tree-Structured Reinforcement Learning for Sequential Object Localization Zequn Jie, Xiaodan Liang, Jiashi Feng, Xiaojie Jin, Wen Feng Lu, Shuicheng Yan |
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Learning brain regions via large-scale online structured sparse dictionary learning Elvis Dohmatob, Arthur Mensch, Gaël Varoquaux, Bertrand Thirion |
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Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds Hongyi Zhang, Sashank J. Reddi, Suvrit Sra |
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Xu Jia, Bert De Brabandere, Tinne Tuytelaars, Luc Van Gool |
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Infinite Hidden Semi-Markov Modulated Interaction Point Process Peng Lin, Bang Zhang, Ting Guo, Yang Wang, Fang Chen |
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Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling Maria-Florina Balcan, Hongyang Zhang |
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Ashish Kapoor, Nathan Wiebe, Krysta M. Svore |
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Mixed vine copulas as joint models of spike counts and local field potentials Arno Onken, Stefano Panzeri |
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Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy |
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Unsupervised Learning from Noisy Networks with Applications to Hi-C Data Bo Wang, Junjie Zhu, Armin Pourshafeie, Oana Ursu, Serafim Batzoglou, Anshul Kundaje |
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Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, Josh Tenenbaum |
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Diffusion-Convolutional Neural Networks James Atwood, Don Towsley |
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A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification Steven Cheng-Xian Li, Benjamin M. Marlin |
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Learning a Metric Embedding for Face Recognition using the Multibatch Method Oren Tadmor, Tal Rosenwein, Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua |
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Learning in Games: Robustness of Fast Convergence Dylan J. Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Éva Tardos |
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Quantized Random Projections and Non-Linear Estimation of Cosine Similarity Ping Li, Michael Mitzenmacher, Martin Slawski |
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Maximal Sparsity with Deep Networks? Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf, Baoyuan Wang |
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Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire |
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Direct Feedback Alignment Provides Learning in Deep Neural Networks Arild Nøkland |
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Adaptive Smoothed Online Multi-Task Learning Keerthiram Murugesan, Hanxiao Liu, Jaime G. Carbonell, Yiming Yang |
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Subhashini Krishnasamy, Rajat Sen, Ramesh Johari, Sanjay Shakkottai |
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Beyond Exchangeability: The Chinese Voting Process Moontae Lee, Seok Hyun Jin, David M. Mimno |
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Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Bernhard Schölkopf |
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Learning feed-forward one-shot learners Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip H. S. Torr, Andrea Vedaldi |
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Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images Junhua Mao, Jiajing Xu, Yushi Jing, Alan L. Yuille |
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Online Pricing with Strategic and Patient Buyers Michal Feldman, Tomer Koren, Roi Livni, Yishay Mansour, Aviv Zohar |
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Completely random measures for modelling block-structured sparse networks Tue Herlau, Mikkel N. Schmidt, Morten Mørup |
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Efficient Neural Codes under Metabolic Constraints Zhuo Wang, Xue-Xin Wei, Alan A. Stocker, Daniel D. Lee |
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Damek Davis, Brent Edmunds, Madeleine Udell |
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Double Thompson Sampling for Dueling Bandits Huasen Wu, Xin Liu |
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Accelerating Stochastic Composition Optimization Mengdi Wang, Ji Liu, Ethan X. Fang |
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MetaGrad: Multiple Learning Rates in Online Learning Tim van Erven, Wouter M. Koolen |
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Spatiotemporal Residual Networks for Video Action Recognition Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes |
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Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models Tomoharu Iwata, Makoto Yamada |
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Can Peripheral Representations Improve Clutter Metrics on Complex Scenes? Arturo Deza, Miguel P. Eckstein |
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Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences Daniel Neil, Michael Pfeiffer, Shih-Chii Liu |
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Normalized Spectral Map Synchronization Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi |
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