International Conference on Machine Learning, ICML 2016

Model-Free Trajectory Optimization for Reinforcement Learning

Riad Akrour, Gerhard Neumann, Hany Abdulsamad, Abbas Abdolmaleki

Model-Free Trajectory Optimization for Reinforcement Learning

Provable Non-convex Phase Retrieval with Outliers: Median TruncatedWirtinger Flow

Huishuai Zhang, Yuejie Chi, Yingbin Liang

Provable Non-convex Phase Retrieval with Outliers: Median TruncatedWirtinger Flow

Learning Representations for Counterfactual Inference

Fredrik D. Johansson, Uri Shalit, David A. Sontag

Learning Representations for Counterfactual Inference

• https://github.com/vdorie/npci

No-Regret Algorithms for Heavy-Tailed Linear Bandits

Andres Muñoz Medina, Scott Yang

No-Regret Algorithms for Heavy-Tailed Linear Bandits

Why Regularized Auto-Encoders learn Sparse Representation?

Devansh Arpit, Yingbo Zhou, Hung Q. Ngo, Venu Govindaraju

Why Regularized Auto-Encoders learn Sparse Representation?

Bidirectional Helmholtz Machines

Jörg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio

Bidirectional Helmholtz Machines

• https://github.com/jbornschein/bihm

Collapsed Variational Inference for Sum-Product Networks

Han Zhao, Tameem Adel, Geoffrey J. Gordon, Brandon Amos

Collapsed Variational Inference for Sum-Product Networks

Pareto Frontier Learning with Expensive Correlated Objectives

Amar Shah, Zoubin Ghahramani

Pareto Frontier Learning with Expensive Correlated Objectives

Control of Memory, Active Perception, and Action in Minecraft

Junhyuk Oh, Valliappa Chockalingam, Satinder P. Singh, Honglak Lee

Control of Memory, Active Perception, and Action in Minecraft

• https://sites.google.com/a/umich.edu/

A Variational Analysis of Stochastic Gradient Algorithms

Stephan Mandt, Matthew D. Hoffman, David M. Blei

A Variational Analysis of Stochastic Gradient Algorithms

Hierarchical Compound Poisson Factorization

Mehmet Emin Basbug, Barbara E. Engelhardt

Hierarchical Compound Poisson Factorization

Why Most Decisions Are Easy in Tetris - And Perhaps in Other Sequential Decision Problems, As Well

Özgür Simsek, Simón Algorta, Amit Kothiyal

Why Most Decisions Are Easy in Tetris - And Perhaps in Other Sequential Decision Problems, As Well

On the Consistency of Feature Selection With Lasso for Non-linear Targets

Yue Zhang, Weihong Guo, Soumya Ray

On the Consistency of Feature Selection With Lasso for Non-linear Targets

A Random Matrix Approach to Echo-State Neural Networks

Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi

A Random Matrix Approach to Echo-State Neural Networks

Early and Reliable Event Detection Using Proximity Space Representation

Maxime Sangnier, Jérôme Gauthier, Alain Rakotomamonjy

Early and Reliable Event Detection Using Proximity Space Representation

Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods

Huikang Liu, Weijie Wu, Anthony Man-Cho So

Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods

Community Recovery in Graphs with Locality

Yuxin Chen, Govinda M. Kamath, Changho Suh, David Tse

Community Recovery in Graphs with Locality

Revisiting Semi-Supervised Learning with Graph Embeddings

Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov

Revisiting Semi-Supervised Learning with Graph Embeddings

• https://github.com/phanein/deepwalk

Large-Margin Softmax Loss for Convolutional Neural Networks

Weiyang Liu, Yandong Wen, Zhiding Yu, Meng Yang

Large-Margin Softmax Loss for Convolutional Neural Networks

Minimizing the Maximal Loss: How and Why

Shai Shalev-Shwartz, Yonatan Wexler

Minimizing the Maximal Loss: How and Why

Benchmarking Deep Reinforcement Learning for Continuous Control

Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel

Benchmarking Deep Reinforcement Learning for Continuous Control

• http://rlpark.github.io
• http://code.google.com/p/beliefbox/

Discrete Deep Feature Extraction: A Theory and New Architectures

Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Bölcskei

Discrete Deep Feature Extraction: A Theory and New Architectures

• http://www.nari.ee.ethz.ch/commth/research/

Continuous Deep Q-Learning with Model-based Acceleration

Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine

Continuous Deep Q-Learning with Model-based Acceleration

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM

Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM

Exploiting Cyclic Symmetry in Convolutional Neural Networks

Sander Dieleman, Jeffrey De Fauw, Koray Kavukcuoglu

Exploiting Cyclic Symmetry in Convolutional Neural Networks

Linking losses for density ratio and class-probability estimation

Aditya Krishna Menon, Cheng Soon Ong

Linking losses for density ratio and class-probability estimation

The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks

Yingfei Wang, Chu Wang, Warren B. Powell

The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies

David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies

Hyperparameter optimization with approximate gradient

Fabian Pedregosa

Hyperparameter optimization with approximate gradient

• https://github.com/fabianp/hoag

The Teaching Dimension of Linear Learners

Ji Liu, Xiaojin Zhu, Hrag Ohannessian

The Teaching Dimension of Linear Learners

SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization

Zheng Qu, Peter Richtárik, Martin Takác, Olivier Fercoq

SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization

Rich Component Analysis

Rong Ge, James Zou

Rich Component Analysis

Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling

Zeyuan Allen Zhu, Zheng Qu, Peter Richtárik, Yang Yuan

Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling

ADIOS: Architectures Deep In Output Space

Moustapha Cissé, Maruan Al-Shedivat, Samy Bengio

ADIOS: Architectures Deep In Output Space

• https://github.com/alshedivat/adios

Analysis of Deep Neural Networks with Extended Data Jacobian Matrix

Shengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff A. Bilmes, Matthai Philipose, Matthew Richardson, Krzysztof Geras, Gregor Urban, Özlem Aslan

Analysis of Deep Neural Networks with Extended Data Jacobian Matrix

Complex Embeddings for Simple Link Prediction

Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard

Complex Embeddings for Simple Link Prediction

• https://github.com/ttrouill/complex

BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits

Alexander Rakhlin, Karthik Sridharan

BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits

Beyond CCA: Moment Matching for Multi-View Models

Anastasia Podosinnikova, Francis R. Bach, Simon Lacoste-Julien

Beyond CCA: Moment Matching for Multi-View Models

• https://github.com/anastasia-podosinnikova/cca

Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling

Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi

Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling

• https://github.com/takeuchi-lab/s3fs

Slice Sampling on Hamiltonian Trajectories

Benjamin Bloem-Reddy, John Cunningham

Slice Sampling on Hamiltonian Trajectories

Learning to Filter with Predictive State Inference Machines

Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell

Learning to Filter with Predictive State Inference Machines

Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations

Aaron Schein, Mingyuan Zhou, David M. Blei, Hanna M. Wallach

Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations

Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model

Xinze Guan, Raviv Raich, Weng-Keen Wong

Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model

Principal Component Projection Without Principal Component Analysis

Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford

Principal Component Projection Without Principal Component Analysis

Contextual Combinatorial Cascading Bandits

Shuai Li, Baoxiang Wang, Shengyu Zhang, Wei Chen

Contextual Combinatorial Cascading Bandits

Generalized Direct Change Estimation in Ising Model Structure

Farideh Fazayeli, Arindam Banerjee

Generalized Direct Change Estimation in Ising Model Structure

Stability of Controllers for Gaussian Process Forward Models

Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Anne Romer, Henner Schmidt, Jan Peters

Stability of Controllers for Gaussian Process Forward Models

Variable Elimination in the Fourier Domain

Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla P. Gomes, Bart Selman

Variable Elimination in the Fourier Domain

Primal-Dual Rates and Certificates

Celestine Dünner, Simone Forte, Martin Takác, Martin Jaggi

Primal-Dual Rates and Certificates

Stochastic Block BFGS: Squeezing More Curvature out of Data

Robert M. Gower, Donald Goldfarb, Peter Richtárik

Stochastic Block BFGS: Squeezing More Curvature out of Data

On the Iteration Complexity of Oblivious First-Order Optimization Algorithms

Yossi Arjevani, Ohad Shamir

On the Iteration Complexity of Oblivious First-Order Optimization Algorithms

Loss factorization, weakly supervised learning and label noise robustness

Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni

Loss factorization, weakly supervised learning and label noise robustness

Doubly Decomposing Nonparametric Tensor Regression

Masaaki Imaizumi, Kohei Hayashi

Doubly Decomposing Nonparametric Tensor Regression

Robust Principal Component Analysis with Side Information

Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon

Robust Principal Component Analysis with Side Information

Fast DPP Sampling for Nystrom with Application to Kernel Methods

Chengtao Li, Stefanie Jegelka, Suvrit Sra

Fast DPP Sampling for Nystrom with Application to Kernel Methods

Variance Reduction for Faster Non-Convex Optimization

Zeyuan Allen Zhu, Elad Hazan

Variance Reduction for Faster Non-Convex Optimization

Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity

Quanming Yao, James T. Kwok

Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity

• https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html

Pixel Recurrent Neural Networks

Aäron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu

Pixel Recurrent Neural Networks

Sequence to Sequence Training of CTC-RNNs with Partial Windowing

Kyuyeon Hwang, Wonyong Sung

Sequence to Sequence Training of CTC-RNNs with Partial Windowing

Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit

Jie Shen, Ping Li, Huan Xu

Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit

Partition Functions from Rao-Blackwellized Tempered Sampling

David E. Carlson, Patrick Stinson, Ari Pakman, Liam Paninski

Partition Functions from Rao-Blackwellized Tempered Sampling

• https://github.com/choderalab/pymbar
• http://www.cs.toronto.edu/~rsalakhu/rbm_ais.html

A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery

Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit S. Dhillon

A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery

A Convolutional Attention Network for Extreme Summarization of Source Code

Miltiadis Allamanis, Hao Peng, Charles A. Sutton

A Convolutional Attention Network for Extreme Summarization of Source Code

• http://groups.inf.ed.ac.uk/cup/codeattention/
• https://github.com/mast-group/convolutional-attention

Learning Granger Causality for Hawkes Processes

Hongteng Xu, Mehrdad Farajtabar, Hongyuan Zha

Learning Granger Causality for Hawkes Processes

• https://sites.google.com/site/htxu313/resources/software

Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner

Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Convolutional Rectifier Networks as Generalized Tensor Decompositions

Nadav Cohen, Amnon Shashua

Convolutional Rectifier Networks as Generalized Tensor Decompositions

Generalization and Exploration via Randomized Value Functions

Ian Osband, Benjamin Van Roy, Zheng Wen

Generalization and Exploration via Randomized Value Functions

DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression

Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh

DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression

• https://github.com/jovana-mitrovic/dr-abc

Nonlinear Statistical Learning with Truncated Gaussian Graphical Models

Qinliang Su, Xuejun Liao, Changyou Chen, Lawrence Carin

Nonlinear Statistical Learning with Truncated Gaussian Graphical Models

Fast k-means with accurate bounds

James Newling, François Fleuret

Fast k-means with accurate bounds

• https://github.com/idiap/eakmeans

Asynchronous Methods for Deep Reinforcement Learning

Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

Asynchronous Methods for Deep Reinforcement Learning

Preconditioning Kernel Matrices

Kurt Cutajar, Michael A. Osborne, John P. Cunningham, Maurizio Filippone

Preconditioning Kernel Matrices

• http://github.com/mauriziofilippone/preconditioned_GPs

Variational Inference for Monte Carlo Objectives

Andriy Mnih, Danilo Jimenez Rezende

Variational Inference for Monte Carlo Objectives

Stochastic Quasi-Newton Langevin Monte Carlo

Umut Simsekli, Roland Badeau, A. Taylan Cemgil, Gaël Richard

Stochastic Quasi-Newton Langevin Monte Carlo

Associative Long Short-Term Memory

Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves

Associative Long Short-Term Memory

Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity

Ohad Shamir

Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity

One-Shot Generalization in Deep Generative Models

Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra

One-Shot Generalization in Deep Generative Models

Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling

Christopher De Sa, Christopher Ré, Kunle Olukotun

Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling

Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference

Tudor Achim, Ashish Sabharwal, Stefano Ermon

Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference

Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series

Yunseong Hwang, Anh Tong, Jaesik Choi

Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series

Interacting Particle Markov Chain Monte Carlo

Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank D. Wood

Interacting Particle Markov Chain Monte Carlo

• https://bitbucket.org/twgr/ipmcmc
• http://www.robots.ox.ac.uk/~fwood/anglican/

Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives

Zeyuan Allen Zhu, Yang Yuan

Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives

Convergence of Stochastic Gradient Descent for PCA

Ohad Shamir

Convergence of Stochastic Gradient Descent for PCA

On the Quality of the Initial Basin in Overspecified Neural Networks

Itay Safran, Ohad Shamir

On the Quality of the Initial Basin in Overspecified Neural Networks

Learning Simple Algorithms from Examples

Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus

Learning Simple Algorithms from Examples

Learning and Inference via Maximum Inner Product Search

Stephen Mussmann, Stefano Ermon

Learning and Inference via Maximum Inner Product Search

CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy

Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E. Lauter, Michael Naehrig, John Wernsing

CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy

Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation

David P. Wipf

Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation

A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums

Anton Rodomanov, Dmitry Kropotov

A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Yarin Gal, Zoubin Ghahramani

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Expressiveness of Rectifier Networks

Xingyuan Pan, Vivek Srikumar

Expressiveness of Rectifier Networks

Stochastic Variance Reduction for Nonconvex Optimization

Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola

Stochastic Variance Reduction for Nonconvex Optimization

Opponent Modeling in Deep Reinforcement Learning

He He, Jordan L. Boyd-Graber

Opponent Modeling in Deep Reinforcement Learning

• https://github.com/hhexiy/

Markov-modulated Marked Poisson Processes for Check-in Data

Jiangwei Pan, Vinayak A. Rao, Pankaj K. Agarwal, Alan E. Gelfand

Markov-modulated Marked Poisson Processes for Check-in Data

Estimating Cosmological Parameters from the Dark Matter Distribution

Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos

Estimating Cosmological Parameters from the Dark Matter Distribution

A ranking approach to global optimization

Cédric Malherbe, Emile Contal, Nicolas Vayatis

A ranking approach to global optimization

Discrete Distribution Estimation under Local Privacy

Peter Kairouz, Keith Bonawitz, Daniel Ramage

Discrete Distribution Estimation under Local Privacy

Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier

Jacob D. Abernethy, Elad Hazan

Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier

Learning Mixtures of Plackett-Luce Models

Zhibing Zhao, Peter Piech, Lirong Xia

Learning Mixtures of Plackett-Luce Models

Differentially Private Policy Evaluation

Borja Balle, Maziar Gomrokchi, Doina Precup

Differentially Private Policy Evaluation

Model-Free Imitation Learning with Policy Optimization

Jonathan Ho, Jayesh K. Gupta, Stefano Ermon

Model-Free Imitation Learning with Policy Optimization

Deep Structured Energy Based Models for Anomaly Detection

Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang

Deep Structured Energy Based Models for Anomaly Detection

Additive Approximations in High Dimensional Nonparametric Regression via the SALSA

Kirthevasan Kandasamy, Yaoliang Yu

Additive Approximations in High Dimensional Nonparametric Regression via the SALSA

Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning

Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis D. Haupt

Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning

Gromov-Wasserstein Averaging of Kernel and Distance Matrices

Gabriel Peyré, Marco Cuturi, Justin Solomon

Gromov-Wasserstein Averaging of Kernel and Distance Matrices

• https://github.com/gpeyre/

Extreme F-measure Maximization using Sparse Probability Estimates

Kalina Jasinska, Krzysztof Dembczynski, Róbert Busa-Fekete, Karlson Pfannschmidt, Timo Klerx, Eyke Hüllermeier

Extreme F-measure Maximization using Sparse Probability Estimates

• https://github.com/busarobi/XMLC

Asymmetric Multi-task Learning based on Task Relatedness and Confidence

Giwoong Lee, Eunho Yang, Sung Ju Hwang

Asymmetric Multi-task Learning based on Task Relatedness and Confidence

Representational Similarity Learning with Application to Brain Networks

Urvashi Oswal, Christopher R. Cox, Matthew A. Lambon Ralph, Timothy T. Rogers, Robert D. Nowak

Representational Similarity Learning with Application to Brain Networks

Heteroscedastic Sequences: Beyond Gaussianity

Oren Anava, Shie Mannor

Heteroscedastic Sequences: Beyond Gaussianity

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow

Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Ben Recht

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow

Training Deep Neural Networks via Direct Loss Minimization

Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun

Training Deep Neural Networks via Direct Loss Minimization

Stochastic Discrete Clenshaw-Curtis Quadrature

Nico Piatkowski, Katharina Morik

Stochastic Discrete Clenshaw-Curtis Quadrature

A Kronecker-factored approximate Fisher matrix for convolution layers

Roger B. Grosse, James Martens

A Kronecker-factored approximate Fisher matrix for convolution layers

Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm

Junpei Komiyama, Junya Honda, Hiroshi Nakagawa

Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm

Algorithms for Optimizing the Ratio of Submodular Functions

Wenruo Bai, Rishabh K. Iyer, Kai Wei, Jeff A. Bilmes

Algorithms for Optimizing the Ratio of Submodular Functions

Multi-Player Bandits - a Musical Chairs Approach

Jonathan Rosenski, Ohad Shamir, Liran Szlak

Multi-Player Bandits - a Musical Chairs Approach

Unitary Evolution Recurrent Neural Networks

Martín Arjovsky, Amar Shah, Yoshua Bengio

Unitary Evolution Recurrent Neural Networks

Dueling Network Architectures for Deep Reinforcement Learning

Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas

Dueling Network Architectures for Deep Reinforcement Learning

Doubly Robust Off-policy Value Evaluation for Reinforcement Learning

Nan Jiang, Lihong Li

Doubly Robust Off-policy Value Evaluation for Reinforcement Learning

Autoencoding beyond pixels using a learned similarity metric

Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther

Autoencoding beyond pixels using a learned similarity metric

• http://github.com/andersbll/
• http://github.com/andersbll/deeppy

Estimation from Indirect Supervision with Linear Moments

Aditi Raghunathan, Roy Frostig, John C. Duchi, Percy Liang

Estimation from Indirect Supervision with Linear Moments

Estimating Structured Vector Autoregressive Models

Igor Melnyk, Arindam Banerjee

Estimating Structured Vector Autoregressive Models

Energetic Natural Gradient Descent

Philip S. Thomas, Bruno Castro da Silva, Christoph Dann, Emma Brunskill

Energetic Natural Gradient Descent

Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification

Yuting Zhang, Kibok Lee, Honglak Lee

Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification

• https://github.com/YutingZhang/caffe-recon-dec

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Barron and Cover's Theory in Supervised Learning and its Application to Lasso

Masanori Kawakita, Jun'ichi Takeuchi

Barron and Cover's Theory in Supervised Learning and its Application to Lasso

Conditional Bernoulli Mixtures for Multi-label Classification

Cheng Li, Bingyu Wang, Virgil Pavlu, Javed A. Aslam

Conditional Bernoulli Mixtures for Multi-label Classification

• https://github.com/cheng-li/pyramid

DCM Bandits: Learning to Rank with Multiple Clicks

Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Zheng Wen

DCM Bandits: Learning to Rank with Multiple Clicks

How to Fake Multiply by a Gaussian Matrix

Michael Kapralov, Vamsi K. Potluru, David P. Woodruff

How to Fake Multiply by a Gaussian Matrix

• https://github.com/marinkaz/nimfa

Recovery guarantee of weighted low-rank approximation via alternating minimization

Yuanzhi Li, Yingyu Liang, Andrej Risteski

Recovery guarantee of weighted low-rank approximation via alternating minimization

Adaptive Algorithms for Online Convex Optimization with Long-term Constraints

Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau

Adaptive Algorithms for Online Convex Optimization with Long-term Constraints

A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models

Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low

A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models

Learning Population-Level Diffusions with Generative RNNs

Tatsunori B. Hashimoto, David K. Gifford, Tommi S. Jaakkola

Learning Population-Level Diffusions with Generative RNNs

Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues

Nihar B. Shah, Sivaraman Balakrishnan, Aditya Guntuboyina, Martin J. Wainwright

Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues

Strongly-Typed Recurrent Neural Networks

David Balduzzi, Muhammad Ghifary

Strongly-Typed Recurrent Neural Networks

Anytime optimal algorithms in stochastic multi-armed bandits

Rémy Degenne, Vianney Perchet

Anytime optimal algorithms in stochastic multi-armed bandits

Extended and Unscented Kitchen Sinks

Edwin V. Bonilla, Daniel M. Steinberg, Alistair Reid

Extended and Unscented Kitchen Sinks

Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing

Ke Li, Jitendra Malik

Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing

Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics

Anirban Roychowdhury, Brian Kulis, Srinivasan Parthasarathy

Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics

Conservative Bandits

Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvári

Conservative Bandits

Recurrent Orthogonal Networks and Long-Memory Tasks

Mikael Henaff, Arthur Szlam, Yann LeCun

Recurrent Orthogonal Networks and Long-Memory Tasks

Predictive Entropy Search for Multi-objective Bayesian Optimization

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams

Predictive Entropy Search for Multi-objective Bayesian Optimization

• https://github.com/HIPS/

Faster Eigenvector Computation via Shift-and-Invert Preconditioning

Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford

Faster Eigenvector Computation via Shift-and-Invert Preconditioning

Deconstructing the Ladder Network Architecture

Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron C. Courville, Yoshua Bengio

Deconstructing the Ladder Network Architecture

Estimating Maximum Expected Value through Gaussian Approximation

Carlo D'Eramo, Marcello Restelli, Alessandro Nuara

Estimating Maximum Expected Value through Gaussian Approximation

Controlling the distance to a Kemeny consensus without computing it

Yunlong Jiao, Anna Korba, Eric Sibony

Controlling the distance to a Kemeny consensus without computing it

Solving Ridge Regression using Sketched Preconditioned SVRG

Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz

Solving Ridge Regression using Sketched Preconditioned SVRG

Recommendations as Treatments: Debiasing Learning and Evaluation

Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims

Recommendations as Treatments: Debiasing Learning and Evaluation

• https://www.cs.cornell.edu/schnabts/mnar/

On collapsed representation of hierarchical Completely Random Measures

Gaurav Pandey, Ambedkar Dukkipati

On collapsed representation of hierarchical Completely Random Measures

Hawkes Processes with Stochastic Excitations

Young Lee, Kar Wai Lim, Cheng Soon Ong

Hawkes Processes with Stochastic Excitations

Tensor Decomposition via Joint Matrix Schur Decomposition

Nicolò Colombo, Nikos Vlassis

Tensor Decomposition via Joint Matrix Schur Decomposition

Robust Random Cut Forest Based Anomaly Detection on Streams

Sudipto Guha, Nina Mishra, Gourav Roy, Okke Schrijvers

Robust Random Cut Forest Based Anomaly Detection on Streams

Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning

Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov

Meta-Gradient Boosted Decision Tree Model for Weight and Target Learning

• https://github.com/dmlc/xgboost

Uprooting and Rerooting Graphical Models

Adrian Weller

Uprooting and Rerooting Graphical Models

Mixture Proportion Estimation via Kernel Embeddings of Distributions

Harish G. Ramaswamy, Clayton Scott, Ambuj Tewari

Mixture Proportion Estimation via Kernel Embeddings of Distributions

Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin

Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse H. Engel, Linxi Fan, Christopher Fougner, Awni Y. Hannun, Billy Jun, Tony Han, Patrick LeGresley, Xiangang Li, Libby Lin, Sharan Narang, Andrew Y. Ng, Sherjil Ozair, Ryan Prenger, Sheng Qian, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Chong Wang, Yi Wang, Zhiqian Wang, Bo Xiao, Yan Xie, Dani Yogatama, Jun Zhan, Zhenyao Zhu

Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin

Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization

Eric Balkanski, Baharan Mirzasoleiman, Andreas Krause, Yaron Singer

Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization

PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification

Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, Inderjit S. Dhillon

PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification

• http://www.cs.utexas.edu/~xrhuang/PDSparse/index.html

L1-regularized Neural Networks are Improperly Learnable in Polynomial Time

Yuchen Zhang, Jason D. Lee, Michael I. Jordan

L1-regularized Neural Networks are Improperly Learnable in Polynomial Time

Recycling Randomness with Structure for Sublinear time Kernel Expansions

Krzysztof Choromanski, Vikas Sindhwani

Recycling Randomness with Structure for Sublinear time Kernel Expansions

Pliable Rejection Sampling

Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric-Ambrym Maillard

Pliable Rejection Sampling