Conference on Uncertainty in Artificial Intelligence, UAI 2017

Online Constrained Model-based Reinforcement Learning

Benjamin van Niekerk, Andreas C. Damianou, Benjamin Rosman

Online Constrained Model-based Reinforcement Learning

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

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

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

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

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

• http://www.cs.unh.edu/~ruml/papers/bandits-uai-2017.pdf

Adversarial Sets for Regularising Neural Link Predictors

Pasquale Minervini, Thomas Demeester, Tim Rocktäschel, Sebastian Riedel

Adversarial Sets for Regularising Neural Link Predictors

• https://github.com/uclmr/inferbeddings

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

• https://github.com/trappmartin/SSLSPN_UAI2017

Interpreting Lion Behaviour as Probabilistic Programs

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

Interpreting Lion Behaviour as Probabilistic Programs

FROSH: FasteR Online Sketching Hashing

Xixian Chen, Irwin King, Michael R. Lyu

FROSH: FasteR Online Sketching Hashing

Approximate Evidential Reasoning Using Local Conditioning and Conditional Belief Functions

Van Nguyen

Approximate Evidential Reasoning Using Local Conditioning and Conditional Belief Functions

SAT-Based Causal Discovery under Weaker Assumptions

Zhalama, Jiji Zhang, Frederick Eberhardt, Wolfgang Mayer

SAT-Based Causal Discovery under Weaker Assumptions

Learning Treatment-Response Models from Multivariate Longitudinal Data

Hossein Soleimani, Adarsh Subbaswamy, Suchi Saria

Learning Treatment-Response Models from Multivariate Longitudinal Data

Probabilistic Program Abstractions

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

Probabilistic Program Abstractions

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

A Fast Algorithm for Matrix Eigen-decompositionn

Zhiqiang Xu, Yiping Ke, Xin Gao

A Fast Algorithm for Matrix Eigen-decompositionn

Efficient solutions for Stochastic Shortest Path Problems with Dead Ends

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

Efficient solutions for Stochastic Shortest Path Problems with Dead Ends

Differentially Private Variational Inference for Non-conjugate Models

Joonas Jälkö, Antti Honkela, Onur Dikmen

Differentially Private Variational Inference for Non-conjugate Models

• https://github.com/DPBayes/DPVI-code

Improving Optimization-Based Approximate Inference by Clamping Variables

Junyao Zhao, Josip Djolonga, Sebastian Tschiatschek, Andreas Krause

Improving Optimization-Based Approximate Inference by Clamping Variables

Bayesian Inference of Log Determinants

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

Bayesian Inference of Log Determinants

Learning to Acquire Information

Yewen Pu, Leslie Pack Kaelbling, Armando Solar-Lezama

Learning to Acquire Information

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

Counting Markov Equivalence Classes by Number of Immoralities

Adityanarayanan Radhakrishnan, Liam Solus, Caroline Uhler

Counting Markov Equivalence Classes by Number of Immoralities

• https://github.com/aradha/mec_generation_tool

Holographic Feature Representations of Deep Networks

Martin A. Zinkevich, Alex Davies, Dale Schuurmans

Holographic Feature Representations of Deep Networks

Stochastic Bandit Models for Delayed Conversions

Claire Vernade, Olivier Cappé, Vianney Perchet

Stochastic Bandit Models for Delayed Conversions

Causal Discovery from Temporally Aggregated Time Series

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

Causal Discovery from Temporally Aggregated Time Series

Towards Conditional Independence Test for Relational Data

Sanghack Lee, Vasant G. Honavar

Towards Conditional Independence Test for Relational Data

• https://github.com/sanghack81/KRCIT
• https://github.com/sanghack81/SDCIT

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

Composing Inference Algorithms as Program Transformations

Robert Zinkov, Chung-chieh Shan

Composing Inference Algorithms as Program Transformations

• https://github.com/hakaru-dev/hakaru

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

Learning the Structure of Probabilistic Sentential Decision Diagrams

Yitao Liang, Jessa Bekker, Guy Van den Broeck

Learning the Structure of Probabilistic Sentential Decision Diagrams

• https://github.com/UCLA-StarAI/LearnPSDD

Neighborhood Regularized l^1-Graph

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

Neighborhood Regularized l^1-Graph

Shortest Path under Uncertainty: Exploration versus Exploitation

Zhan Wei Lim, David Hsu, Wee Sun Lee

Shortest Path under Uncertainty: Exploration versus Exploitation

Stochastic Segmentation Trees for Multiple Ground Truths

Jake Snell, Richard S. Zemel

Stochastic Segmentation Trees for Multiple Ground Truths

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

• https://github.com/cgnorthcutt/cleanlab
• cgn@mit.edu

Complexity of Solving Decision Trees with Skew-Symmetric Bilinear Utility

Hugo Gilbert, Olivier Spanjaard

Complexity of Solving Decision Trees with Skew-Symmetric Bilinear Utility

Weighted Model Counting With Function Symbols

Vaishak Belle

Weighted Model Counting With Function Symbols

The Total Belief Theorem

Chunlai Zhou, Fabio Cuzzolin

The Total Belief Theorem

Convex-constrained Sparse Additive Modeling and Its Extensions

Junming Yin, Yaoliang Yu

Convex-constrained Sparse Additive Modeling and Its Extensions

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

Near-Orthogonality Regularization in Kernel Methods

Pengtao Xie, Barnabás Póczos, Eric P. Xing

Near-Orthogonality Regularization in Kernel Methods

On the Complexity of Nash Equilibrium Reoptimization

Andrea Celli, Alberto Marchesi, Nicola Gatti

On the Complexity of Nash Equilibrium Reoptimization

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