UAI 2019

119 papers

Exclusivity Graph Approach to Instrumental Inequalities

Davide Poderini, Rafael Chaves, Iris Agresti, Gonzalo Carvacho, Fabio Sciarrino

Block Neural Autoregressive Flow

Nicola De Cao, Wilker Aziz, Ivan Titov

Fast Proximal Gradient Descent for A Class of Non-convex and Non-smooth Sparse Learning Problems

Yingzhen Yang, Jiahui Yu

Embarrassingly Parallel MCMC using Deep Invertible Transformations

Diego Mesquita, Paul Blomstedt, Samuel Kaski

On Open-Universe Causal Reasoning

Duligur Ibeling, Thomas Icard

Bayesian Optimization with Binary Auxiliary Information

Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low

Probability Distillation: A Caveat and Alternatives

Chin-Wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron Courville

Variational Inference of Penalized Regression with Submodular Functions

Koh Takeuchi, Yuichi Yoshida, Yoshinobu Kawahara

Co-training for Policy Learning

Jialin Song, ravi lanka, Yisong Yue, Masahiro Ono

Off-Policy Policy Gradient with Stationary Distribution Correction

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

Subspace Inference for Bayesian Deep Learning

Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov

A Weighted Mini-Bucket Bound for Solving Influence Diagram

Junkyu Lee, Radu Marinescu, Alexander Ihler, Rina Dechter

Identification In Missing Data Models Represented By Directed Acyclic Graphs

Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, James M. Robins

Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption

Ondrej Kuzelka, Jesse Davis

Low Frequency Adversarial Perturbation

Chuan Guo, Jared S. Frank, Kilian Q. Weinberger

Interpretable Almost Matching Exactly With Instrumental Variables

M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin

On the Relationship Between Satisfiability and Markov Decision Processes

Ricardo Salmon, Pascal Poupart

Empirical Mechanism Design: Designing Mechanisms from Data

Enrique Areyan Viqueira, Cyrus Cousins, Yasser Mohammad, Amy Greenwald

CCMI : Classifier based Conditional Mutual Information Estimation

Sudipto Mukherjee, Himanshu Asnani, Sreeram Kannan

Object Conditioning for Causal Inference

David Jensen, Javier Burroni, Matthew Rattigan

Learning Belief Representations for Imitation Learning in POMDPs

Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng

Learnability for the Information Bottleneck

Tailin Wu, Ian Fischer, Isaac L. Chuang, Max Tegmark

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow

Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood

Causal Inference Under Interference And Network Uncertainty

Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser

P3O: Policy-on Policy-off Policy Optimization

Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola

Social Reinforcement Learning to Combat Fake News Spread

Mahak Goindani, Jennifer Neville

Real-Time Robotic Search using Structural Spatial Point Processes

Olov Andersson, Per Sidén, Johan Dahlin, Patrick Doherty, Mattias Villani

Generating and Sampling Orbits for Lifted Probabilistic Inference

Steven Holtzen, Todd Millstein, Guy Van den Broeck

Intervening on Network Ties

Eli Sherman, Ilya Shpitser

Exact Sampling of Directed Acyclic Graphs from Modular Distributions

Topi Talvitie, Aleksis Vuoksenmaa, Mikko Koivisto

Approximate Relative Value Learning for Average-reward Continuous State MDPs

Hiteshi Sharma, Mehdi Jafarnia-Jahromi, Rahul Jain

End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations

Gregory Gundersen, Bianca Dumitrascu, Jordan T. Ash, Barbara E. Engelhardt

Convergence Analysis of Gradient-Based Learning in Continuous Games

Benjamin Chasnov, Lillian Ratliff, Eric Mazumdar, Samuel Burden

Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning

Smitha Milli, Anca D. Dragan

Fake It Till You Make It: Learning-Compatible Performance Support

Jonathan Bragg, Emma Brunskill

Noise Contrastive Priors for Functional Uncertainty

Danijar Hafner, Dustin Tran, Timothy Lillicrap, Alex Irpan, James Davidson

On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits

Roman Pogodin, Tor Lattimore

Guaranteed Scalable Learning of Latent Tree Models

Furong Huang, Niranjan Uma Naresh, Ioakeim Perros, Robert Chen, Jimeng Sun

Variational Training for Large-Scale Noisy-OR Bayesian Networks

Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou

Wasserstein Fair Classification

Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa

Problem-dependent Regret Bounds for Online Learning with Feedback Graphs

Bingshan Hu, Nishant A. Mehta, Jianping Pan

N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification

Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee

On Densification for Minwise Hashing

Tung Mai, Anup Rao, Matt Kapilevich, Ryan Rossi, Yasin Abbasi-Yadkori

Online Factorization and Partition of Complex Networks by Random Walk

Lin Yang, zheng Yu, Vladimir braverman, Tuo Zhao, Mengdi Wang

Sampling-free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging

Seong Jae Hwang, Ronak R. Mehta, Hyunwoo J. Kim, Sterling C. Johnson, Vikas Singh

Adaptively Truncating Backpropagation Through Time to Control Gradient Bias

Christopher Aicher, Nicholas J. Foti, Emily B. Fox

Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning

Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier, Andrew Gordon Wilson

Efficient Multitask Feature and Relationship Learning

Han Zhao, Otilia Stretcu, Alexander J. Smola, Geoffrey J. Gordon

A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations

Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos

Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation

Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper

How to Exploit Structure while Solving Weighted Model Integration Problems

Samuel Kolb, Pedro Zuidberg Dos Martires, Luc De Raedt

Sinkhorn AutoEncoders

Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav

Cascading Linear Submodular Bandits: Accounting for Position Bias and Diversity in Online Learning to Rank

Gaurush Hiranandani, Harvineet Singh, Prakhar Gupta, Iftikhar Ahamath Burhanuddin, Zheng Wen

Active Multi-Information Source Bayesian Quadrature

Alexandra Gessner, Javier Gonzalez, Maren Mahsereci

Learning with Non-Convex Truncated Losses by SGD

Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin

Variational Sparse Coding

Francesco Tonolini, Bjørn Sand Jensen, Roderick Murray-Smith

Probabilistic Programming for Birth-Death Models of Evolution Using an Alive Particle Filter with Delayed Sampling

Jan Kudlicka, Lawrence M. Murray, Fredrik Ronquist, Thomas B. Schön

Evacuate or Not? A POMDP Model of the Decision Making of Individuals in Hurricane Evacuation Zones

Adithya Raam Sankar, Prashant Doshi, Adam Goodie

Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization

Guanghui Wang, Shiyin Lu, Lijun Zhang

A Bayesian Approach to Robust Reinforcement Learning

Esther Derman, Daniel Mankowitz, Timothy Mann, Shie Mannor

Finding Minimal d-separators in Linear Time and Applications

Benito van der Zander, Maciej Liśkiewicz

Belief Propagation: Accurate Marginals or Accurate Partition Function – Where is the Difference?

Christian Knoll, Franz Pernkopf

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva

Approximate Causal Abstractions

Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern

Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits

Shreyas S, Aadirupa Saha, Chiranjib Bhattacharyya

Beyond Structural Causal Models: Causal Constraints Models

Tineke Blom, Stephan Bongers, Joris M. Mooij

Sliced Score Matching: A Scalable Approach to Density and Score Estimation

Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon

Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation

Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir

Deep Mixture of Experts via Shallow Embedding

Xin Wang, Fisher Yu, Lisa Dunlap, Yi-An Ma, Ruth Wang

An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient

Pan Xu, Felicia Gao, Quanquan Gu

Perturbed-History Exploration in Stochastic Linear Bandits

Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier

A Tighter Analysis of Randomised Policy Iteration

Meet Taraviya, Shivaram Kalyanakrishnan

Augmenting and Tuning Knowledge Graph Embeddings

Robert Bamler, Farnood Salehi, Stephan Mandt

Efficient Neural Network Verification with Exactness Characterization

Krishnamurthy (Dj) Dvijotham, Robert Stanforth, Sven Gowal, Chongli Qin, Soham De

Periodic Kernel Approximation by Index Set Fourier Series Features

Anthony Tompkins, Fabio Ramos

Epsilon-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning

Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee

Fisher-Bures Adversary Graph Convolutional Networks

Ke Sun, Piotr Koniusz, Zhen Wang

Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks

Qi She, Anqi Wu

A Fast Proximal Point Method for Computing Exact Wasserstein Distance

Yujia Xie, Xiangfeng Wang, Ruijia Wang, Hongyuan Zha

Randomized Value Functions via Multiplicative Normalizing Flows

Ahmed Touati, harsh satija, Joshua Romoff, Joelle Pineau, Pascal Vincent

Approximate Inference in Structured Instances with Noisy Categorical Observations

Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas

Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem

Karen Ullrich, Rianne van den Berg, Marcus Brubaker, David Fleet, Max Welling

General Identifiability with Arbitrary Surrogate Experiments

Sanghack Lee, Juan D. Correa, Elias Bareinboim

Joint Nonparametric Precision Matrix Estimation with Confounding

Sinong Geng, Mladen Kolar, Oluwasanmi Koyejo

Random Search and Reproducibility for Neural Architecture Search

Liam Li, Ameet Talwalkar

The Role of Memory in Stochastic Optimization

Antonio Orvieto, Jonas Köhler, Aurelien Lucchi

Towards Robust Relational Causal Discovery

Sanghack Lee, Vasant Honavar

Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Xiaoting Shao

Stability of Linear Structural Equation Models of Causal Inference

Karthik Abhinav Sankararaman, Anand Louis, Navin Goyal

Cubic Regularization with Momentum for Nonconvex Optimization

Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan

Efficient Planning Under Uncertainty with Incremental Refinement

Juan Carlos Saborío, Joachim Hertzberg

Domain Generalization via Multidomain Discriminant Analysis

Shoubo Hu, Kun Zhang, Zhitang Chen, Laiwan Chan

Towards a Better Understanding and Regularization of GAN Training Dynamics

Weili Nie, Ankit B. Patel

Adaptive Hashing for Model Counting

Jonathan Kuck, Tri Dao, Shengjia Zhao, Burak Bartan, Ashish Sabharwal

Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation

Cong Xie, Oluwasanmi Koyejo, Indranil Gupta

Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests

Taoan Huang, Bohui Fang, Xiaohui Bei, Fei Fang

Randomized Iterative Algorithms for Fisher Discriminant Analysis

Agniva Chowdhury, Jiasen Yang, Petros Drineas

Random Clique Covers for Graphs with Local Density and Global Sparsity

Sinead A. Williamson, Mauricio Tec

The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA

Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf

Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory

Philipp Geiger, Michel Besserve, Justus Winkelmann, Claudius Proissl, Bernhard Schölkopf