Conference on Uncertainty in Artificial Intelligence, UAI 2017


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Regret Minimization Algorithms for the Followers Behaviour Identification in Leadership Games

Lorenzo Bisi, Giuseppe De Nittis, Francesco Trovò, Marcello Restelli, Nicola Gatti

Regret Minimization Algorithms for the Followers Behaviour Identification in Leadership Games

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Online Constrained Model-based Reinforcement Learning

Benjamin van Niekerk, Andreas C. Damianou, Benjamin Rosman

Online Constrained Model-based Reinforcement Learning

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Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

Gintare Karolina Dziugaite, Daniel M. Roy

Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

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A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units

Niranjani Prasad, Li-Fang Cheng, Corey Chivers, Michael Draugelis, Barbara E. Engelhardt

A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units

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Stein Variational Adaptive Importance Sampling

Jun Han, Qiang Liu

Stein Variational Adaptive Importance Sampling

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Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search

Stephen Mussmann, Daniel Levy, Stefano Ermon

Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search

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On Loopy Belief Propagation - Local Stability Analysis for Non-Vanishing Fields

Christian Knoll, Franz Pernkopf

On Loopy Belief Propagation - Local Stability Analysis for Non-Vanishing Fields

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Stochastic L-BFGS Revisited: Improved Convergence Rates and Practical Acceleration Strategies

Renbo Zhao, William B. Haskell, Vincent Y. F. Tan

Stochastic L-BFGS Revisited: Improved Convergence Rates and Practical Acceleration Strategies

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Communication-Efficient Distributed Primal-Dual Algorithm for Saddle Point Problem

Yaodong Yu, Sulin Liu, Sinno Jialin Pan

Communication-Efficient Distributed Primal-Dual Algorithm for Saddle Point Problem

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Robust Model Equivalence using Stochastic Bisimulation for N-Agent Interactive DIDs

Muthukumaran Chandrasekaran, Junhuan Zhang, Prashant Doshi, Yifeng Zeng

Robust Model Equivalence using Stochastic Bisimulation for N-Agent Interactive DIDs

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Learning to Draw Samples with Amortized Stein Variational Gradient Descent

Yihao Feng, Dilin Wang, Qiang Liu

Learning to Draw Samples with Amortized Stein Variational Gradient Descent

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Value Directed Exploration in Multi-Armed Bandits with Structured Priors

Bence Cserna, Marek Petrik, Reazul Hasan Russel, Wheeler Ruml

Value Directed Exploration in Multi-Armed Bandits with Structured Priors

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Coupling Adaptive Batch Sizes with Learning Rates

Lukas Balles, Javier Romero, Philipp Hennig

Coupling Adaptive Batch Sizes with Learning Rates

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Adversarial Sets for Regularising Neural Link Predictors

Pasquale Minervini, Thomas Demeester, Tim Rocktäschel, Sebastian Riedel

Adversarial Sets for Regularising Neural Link Predictors

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Provable Inductive Robust PCA via Iterative Hard Thresholding

U. N. Niranjan, Arun Rajkumar, Theja Tulabandhula

Provable Inductive Robust PCA via Iterative Hard Thresholding

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Safe Semi-Supervised Learning of Sum-Product Networks

Martin Trapp, Tamas Madl, Robert Peharz, Franz Pernkopf, Robert Trappl

Safe Semi-Supervised Learning of Sum-Product Networks

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Self-Discrepancy Conditional Independence Test

Sanghack Lee, Vasant G. Honavar

Self-Discrepancy Conditional Independence Test

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Interpreting Lion Behaviour as Probabilistic Programs

Neil Dhir, Matthijs Vákár, Matthew Wijers, Andrew Markham, Frank D. Wood

Interpreting Lion Behaviour as Probabilistic Programs

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A Tractable Probabilistic Model for Subset Selection

Yujia Shen, Arthur Choi, Adnan Darwiche

A Tractable Probabilistic Model for Subset Selection

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FROSH: FasteR Online Sketching Hashing

Xixian Chen, Irwin King, Michael R. Lyu

FROSH: FasteR Online Sketching Hashing

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Analysis of Thompson Sampling for Stochastic Sleeping Bandits

Aritra Chatterjee, Ganesh Ghalme, Shweta Jain, Rohit Vaish, Y. Narahari

Analysis of Thompson Sampling for Stochastic Sleeping Bandits

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Approximate Evidential Reasoning Using Local Conditioning and Conditional Belief Functions

Van Nguyen

Approximate Evidential Reasoning Using Local Conditioning and Conditional Belief Functions

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Continuously Tempered Hamiltonian Monte Carlo

Matthew M. Graham, Amos J. Storkey

Continuously Tempered Hamiltonian Monte Carlo

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SAT-Based Causal Discovery under Weaker Assumptions

Zhalama, Jiji Zhang, Frederick Eberhardt, Wolfgang Mayer

SAT-Based Causal Discovery under Weaker Assumptions

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Embedding Senses via Dictionary Bootstrapping

Byungkon Kang, Kyung-Ah Sohn

Embedding Senses via Dictionary Bootstrapping

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Learning Treatment-Response Models from Multivariate Longitudinal Data

Hossein Soleimani, Adarsh Subbaswamy, Suchi Saria

Learning Treatment-Response Models from Multivariate Longitudinal Data

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Synthesis of Strategies in Influence Diagrams

Manuel Luque, Manuel Arias, Francisco Javier Díez

Synthesis of Strategies in Influence Diagrams

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Probabilistic Program Abstractions

Steven Holtzen, Todd D. Millstein, Guy Van den Broeck

Probabilistic Program Abstractions

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Effective sketching methods for value function approximation

Yangchen Pan, Erfan Sadeqi Azer, Martha White

Effective sketching methods for value function approximation

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Exact Inference for Relational Graphical Models with Interpreted Functions: Lifted Probabilistic Inference Modulo Theories

Rodrigo de Salvo Braz, Ciaran O'Reilly

Exact Inference for Relational Graphical Models with Interpreted Functions: Lifted Probabilistic Inference Modulo Theories

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Data-Dependent Sparsity for Subspace Clustering

Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf

Data-Dependent Sparsity for Subspace Clustering

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A Fast Algorithm for Matrix Eigen-decompositionn

Zhiqiang Xu, Yiping Ke, Xin Gao

A Fast Algorithm for Matrix Eigen-decompositionn

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Iterative Decomposition Guided Variable Neighborhood Search for Graphical Model Energy Minimization

Abdelkader Ouali, David Allouche, Simon de Givry, Samir Loudni, Yahia Lebbah, Lakhdar Loukil

Iterative Decomposition Guided Variable Neighborhood Search for Graphical Model Energy Minimization

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Efficient solutions for Stochastic Shortest Path Problems with Dead Ends

Felipe W. Trevizan, Florent Teichteil-Knigsbuch, Sylvie Thiébaux

Efficient solutions for Stochastic Shortest Path Problems with Dead Ends

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Causal Consistency of Structural Equation Models

Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf

Causal Consistency of Structural Equation Models

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Differentially Private Variational Inference for Non-conjugate Models

Joonas Jälkö, Antti Honkela, Onur Dikmen

Differentially Private Variational Inference for Non-conjugate Models

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Triply Stochastic Gradients on Multiple Kernel Learning

Xiang Li, Bin Gu, Shuang Ao, Huaimin Wang, Charles X. Ling

Triply Stochastic Gradients on Multiple Kernel Learning

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Improving Optimization-Based Approximate Inference by Clamping Variables

Junyao Zhao, Josip Djolonga, Sebastian Tschiatschek, Andreas Krause

Improving Optimization-Based Approximate Inference by Clamping Variables

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Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting

Yuxin Chen, Jean-Michel Renders, Morteza Haghir Chehreghani, Andreas Krause

Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting

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Bayesian Inference of Log Determinants

Jack K. Fitzsimons, Kurt Cutajar, Maurizio Filippone, Michael A. Osborne, Stephen J. Roberts

Bayesian Inference of Log Determinants

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Supervised Restricted Boltzmann Machines

Tu Dinh Nguyen, Dinh Q. Phung, Viet Huynh, Trung Le

Supervised Restricted Boltzmann Machines

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Learning to Acquire Information

Yewen Pu, Leslie Pack Kaelbling, Armando Solar-Lezama

Learning to Acquire Information

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Stein Variational Policy Gradient

Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng

Stein Variational Policy Gradient

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A Practical Method for Solving Contextual Bandit Problems Using Decision Trees

Adam N. Elmachtoub, Ryan McNellis, Sechan Oh, Marek Petrik

A Practical Method for Solving Contextual Bandit Problems Using Decision Trees

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Real-Time Resource Allocation for Tracking Systems

Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek, Henri Bouma

Real-Time Resource Allocation for Tracking Systems

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Counting Markov Equivalence Classes by Number of Immoralities

Adityanarayanan Radhakrishnan, Liam Solus, Caroline Uhler

Counting Markov Equivalence Classes by Number of Immoralities

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Inverse Reinforcement Learning via Deep Gaussian Process

Ming Jin, Andreas C. Damianou, Pieter Abbeel, Costas J. Spanos

Inverse Reinforcement Learning via Deep Gaussian Process

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Holographic Feature Representations of Deep Networks

Martin A. Zinkevich, Alex Davies, Dale Schuurmans

Holographic Feature Representations of Deep Networks

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Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders

Yu Wang, Bin Dai, Gang Hua, John Aston, David P. Wipf

Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders

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Stochastic Bandit Models for Delayed Conversions

Claire Vernade, Olivier Cappé, Vianney Perchet

Stochastic Bandit Models for Delayed Conversions

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Importance Sampling for Fair Policy Selection

Shayan Doroudi, Philip S. Thomas, Emma Brunskill

Importance Sampling for Fair Policy Selection

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Causal Discovery from Temporally Aggregated Time Series

Mingming Gong, Kun Zhang, Bernhard Schölkopf, Clark Glymour, Dacheng Tao

Causal Discovery from Temporally Aggregated Time Series

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Balanced Mini-batch Sampling for SGD Using Determinantal Point Processes

Cheng Zhang, Hedvig Kjellström, Stephan Mandt

Balanced Mini-batch Sampling for SGD Using Determinantal Point Processes

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Towards Conditional Independence Test for Relational Data

Sanghack Lee, Vasant G. Honavar

Towards Conditional Independence Test for Relational Data

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Submodular Variational Inference for Network Reconstruction

Lin Chen, Forrest W. Crawford, Amin Karbasi

Submodular Variational Inference for Network Reconstruction

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Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks

Diarmaid Conaty, Cassio Polpo de Campos, Denis Deratani Mauá

Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks

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Near-Optimal Interdiction of Factored MDPs

Swetasudha Panda, Yevgeniy Vorobeychik

Near-Optimal Interdiction of Factored MDPs

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Composing Inference Algorithms as Program Transformations

Robert Zinkov, Chung-chieh Shan

Composing Inference Algorithms as Program Transformations

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Learning Approximately Objective Priors

Eric T. Nalisnick, Padhraic Smyth

Learning Approximately Objective Priors

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Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges

Marco Eigenmann, Preetam Nandy, Marloes H. Maathuis

Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges

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An Efficient Minibatch Acceptance Test for Metropolis-Hastings

Daniel Seita, Xinlei Pan, Haoyu Chen, John F. Canny

An Efficient Minibatch Acceptance Test for Metropolis-Hastings

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Learning the Structure of Probabilistic Sentential Decision Diagrams

Yitao Liang, Jessa Bekker, Guy Van den Broeck

Learning the Structure of Probabilistic Sentential Decision Diagrams

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Fair Optimal Stopping Policy for Matching with Mediator

Yang Liu

Fair Optimal Stopping Policy for Matching with Mediator

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Neighborhood Regularized l^1-Graph

Yingzhen Yang, Jiashi Feng, Jiahui Yu, Jianchao Yang, Thomas S. Huang

Neighborhood Regularized l^1-Graph

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Feature-to-Feature Regression for a Two-Step Conditional Independence Test

Qinyi Zhang, Sarah Filippi, Seth R. Flaxman, Dino Sejdinovic

Feature-to-Feature Regression for a Two-Step Conditional Independence Test

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Shortest Path under Uncertainty: Exploration versus Exploitation

Zhan Wei Lim, David Hsu, Wee Sun Lee

Shortest Path under Uncertainty: Exploration versus Exploitation

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Interpreting and Using CPDAGs With Background Knowledge

Emilija Perkovic, Markus Kalisch, Marloes H. Maathuis

Interpreting and Using CPDAGs With Background Knowledge

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Stochastic Segmentation Trees for Multiple Ground Truths

Jake Snell, Richard S. Zemel

Stochastic Segmentation Trees for Multiple Ground Truths

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Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning

Volodymyr Kuleshov, Stefano Ermon

Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning

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Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang

Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

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Multi-dueling Bandits with Dependent Arms

Yanan Sui, Vincent Zhuang, Joel W. Burdick, Yisong Yue

Multi-dueling Bandits with Dependent Arms

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Complexity of Solving Decision Trees with Skew-Symmetric Bilinear Utility

Hugo Gilbert, Olivier Spanjaard

Complexity of Solving Decision Trees with Skew-Symmetric Bilinear Utility

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Algebraic Equivalence Class Selection for Linear Structural Equation Models

Thijs van Ommen, Joris M. Mooij

Algebraic Equivalence Class Selection for Linear Structural Equation Models

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Weighted Model Counting With Function Symbols

Vaishak Belle

Weighted Model Counting With Function Symbols

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The Binomial Block Bootstrap Estimator for Evaluating Loss on Dependent Clusters

Matt Barnes, Artur Dubrawski

The Binomial Block Bootstrap Estimator for Evaluating Loss on Dependent Clusters

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The Total Belief Theorem

Chunlai Zhou, Fabio Cuzzolin

The Total Belief Theorem

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How Good Are My Predictions? Efficiently Approximating Precision-Recall Curves for Massive Datasets

Ashish Sabharwal, Hanie Sedghi

How Good Are My Predictions? Efficiently Approximating Precision-Recall Curves for Massive Datasets

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Convex-constrained Sparse Additive Modeling and Its Extensions

Junming Yin, Yaoliang Yu

Convex-constrained Sparse Additive Modeling and Its Extensions

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Branch and Bound for Regular Bayesian Network Structure Learing

Joe Suzuki, Jun Kawahara

Branch and Bound for Regular Bayesian Network Structure Learing

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Why Rules are Complex: Real-Valued Probabilistic Logic Programs are not Fully Expressive

David Buchman, David Poole

Why Rules are Complex: Real-Valued Probabilistic Logic Programs are not Fully Expressive

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Monte-Carlo Tree Search using Batch Value of Perfect Information

Shahaf S. Shperberg, Solomon Eyal Shimony, Ariel Felner

Monte-Carlo Tree Search using Batch Value of Perfect Information

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Near-Orthogonality Regularization in Kernel Methods

Pengtao Xie, Barnabás Póczos, Eric P. Xing

Near-Orthogonality Regularization in Kernel Methods

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Decoupling Homophily and Reciprocity with Latent Space Network Models

Jiasen Yang, Vinayak Rao, Jennifer Neville

Decoupling Homophily and Reciprocity with Latent Space Network Models

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On the Complexity of Nash Equilibrium Reoptimization

Andrea Celli, Alberto Marchesi, Nicola Gatti

On the Complexity of Nash Equilibrium Reoptimization

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Importance Sampled Stochastic Optimization for Variational Inference

Joseph Sakaya, Arto Klami

Importance Sampled Stochastic Optimization for Variational Inference

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AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models

Karl Krauth, Edwin V. Bonilla, Kurt Cutajar, Maurizio Filippone

AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models

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A Probabilistic Framework for Multi-Label Learning with Unseen Labels

Abhilash Gaure, Piyush Rai

A Probabilistic Framework for Multi-Label Learning with Unseen Labels

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