UAI 2020

140 papers

Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets

Jakob Runge

OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation

Hongyu Ren, Yuke Zhu, Jure Leskovec, Animashree Anandkumar, Animesh Garg

Time Series Analysis using a Kernel based Multi-Modal Uncertainty Decomposition Framework

Rishabh Singh, Jose Principe

Locally Masked Convolution for Autoregressive Models

Ajay Jain, Pieter Abbeel, Deepak Pathak

Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals

Noam Finkelstein, Ilya Shpitser

PoRB-Nets: Poisson Process Radial Basis Function Networks

Beau Coker, Melanie Fernandez Pradier, Finale Doshi-velez

Stable Policy Optimization via Off-Policy Divergence Regularization

Ahmed Touati, Amy Zhang, Joelle Pineau, Pascal Vincent

Non Parametric Graph Learning for Bayesian Graph Neural Networks

Soumyasundar Pal, Saber Malekmohammadi, Florence Regol, Yingxue Zhang, Yishi Xu

Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation

Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Molchanov, Dmitry Vetrov

Bounded Rationality in Las Vegas: Probabilistic Finite Automata Play Multi-Armed Bandits

Xinming Liu, Joseph Halpern

Flexible Approximate Inference via Stratified Normalizing Flows

Chris Cundy, Stefano Ermon

Learning to learn generative programs with Memoised Wake-Sleep

Luke Hewitt, Tuan Anh Le, Joshua Tenenbaum

Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings

Tal Friedman, Guy Broeck

Improved Vector Pruning in Exact Algorithms for Solving POMDPs

Eric Hansen, Thomas Bowman

EiGLasso: Scalable Estimation of Cartesian Product of Sparse Inverse Covariance Matrices

Jun Ho Yoon, Seyoung Kim

Automated Dependence Plots

David Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar

Provably Efficient Third-Person Imitation from Offline Observation

Aaron Zweig, Joan Bruna

Mutual Information Based Knowledge Transfer Under State-Action Dimension Mismatch

Michael Wan, Tanmay Gangwani, Jian Peng

Distortion estimates for approximate Bayesian inference

Hanwen Xing, Geoff Nicholls, Jeong (Kate) Lee

Probabilistic Safety for Bayesian Neural Networks

Matthew Wicker, Luca Laurenti, Andrea Patane, Marta Kwiatkowska

On the Relationship Between Probabilistic Circuits and Determinantal Point Processes

Honghua Zhang, Steven Holtzen, Guy Broeck

Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles

Tárik S. Salem, Helge Langseth, Heri Ramampiaro

Estimation Rates for Sparse Linear Cyclic Causal Models

Jan-Christian Huetter, Philippe Rigollet

Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles

Joris M. Mooij, Tom Claassen

Finite-Memory Near-Optimal Learning for Markov Decision Processes with Long-Run Average Reward

Jan Křetínský, Fabian Michel, Lukas Michel, Guillermo Pérez

Model-Augmented Conditional Mutual Information Estimation for Feature Selection

Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum

Flexible Prior Elicitation via the Prior Predictive Distribution

Marcelo Hartmann, Georgi Agiashvili, Paul Bürkner, Arto Klami

Active Learning of Conditional Mean Embeddings via Bayesian Optimisation

Sayak Ray Chowdhury, Rafael Oliveira, Fabio Ramos

Differentially Private Top-k Selection via Stability on Unknown Domain

Ricardo Silva Carvalho, Ke Wang, Lovedeep Gondara, Chunyan Miao

Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits

Aurelien Bibaut, Antoine Chambaz, Mark Laan

Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation

Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

Batch norm with entropic regularization turns deterministic autoencoders into generative models

Amur Ghose, Abdullah Rashwan, Pascal Poupart

Learning LWF Chain Graphs: A Markov Blanket Discovery Approach

Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi

Aisha Mohamed, Shameem Parambath, Zoi Kaoudi, Ashraf Aboulnaga

MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models

Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

Permutation-Based Causal Structure Learning with Unknown Intervention Targets

Chandler Squires, Yuhao Wang, Caroline Uhler

Dueling Posterior Sampling for Preference-Based Reinforcement Learning

Ellen Novoseller, Yibing Wei, Yanan Sui, Yisong Yue, Joel Burdick

Active Model Estimation in Markov Decision Processes

Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric

Sensor Placement for Spatial Gaussian Processes with Integral Observations

Krista Longi, Chang Rajani, Tom Sillanpää, Joni Mäkinen, Timo Rauhala

Structure Learning for Cyclic Linear Causal Models

Carlos Amendola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu

Graphical continuous Lyapunov models

Gherardo Varando, Niels Richard Hansen

99% of Worker-Master Communication in Distributed Optimization Is Not Needed

Konstantin Mishchenko, Filip Hanzely, Peter Richtarik

Multitask Soft Option Learning

Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N Siddharth

Risk Bounds for Low Cost Bipartite Ranking

San Gultekin, John Paisley

Identification and Estimation of Causal Effects Defined by Shift Interventions

Numair Sani, Jaron Lee, Ilya Shpitser

Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series

Hermanni Hälvä, Aapo Hyvärinen

Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models

Zhijian Ou, Yunfu Song

Adapting Text Embeddings for Causal Inference

Victor Veitch, Dhanya Sridhar, David Blei

Semi-bandit Optimization in the Dispersed Setting

Maria-Florina Balcan, Travis Dick, Wesley Pegden

Zeroth Order Non-convex optimization with Dueling-Choice Bandits

Yichong Xu, Aparna Joshi, Aarti Singh, Artur Dubrawski

Online Parameter-Free Learning of Multiple Low Variance Tasks

Giulia Denevi, Massimiliano Pontil, Dimitrios Stamos

Ordering Variables for Weighted Model Integration

Vincent Derkinderen, Evert Heylen, Pedro Zuidberg Dos Martires, Samuel Kolb, Luc Raedt

Adversarial Learning for 3D Matching

Wei Xing, Brian Ziebart

Stochastic Variational Inference for Dynamic Correlated Topic Models

Federico Tomasi, Praveen Chandar, Gal Levy-Fix, Mounia Lalmas-Roelleke, Zhenwen Dai

C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation

Arnab Mondal, Arnab Bhattacharjee, Sudipto Mukherjee, Himanshu Asnani, Sreeram Kannan

Layering-MCMC for Structure Learning in Bayesian Networks

Jussi Viinikka, Mikko Koivisto

No-regret Exploration in Contextual Reinforcement Learning

Aditya Modi, Ambuj Tewari

A Practical Riemannian Algorithm for Computing Dominant Generalized Eigenspace

Zhiqiang Xu, Ping Li

On Counterfactual Explanations under Predictive Multiplicity

Martin Pawelczyk, Klaus Broelemann, Gjergji. Kasneci

Robust k-means++

Amit Deshpande, Praneeth Kacham, Rameshwar Pratap

Deep Sigma Point Processes

Martin Jankowiak, Geoff Pleiss, Jacob Gardner

Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation

Marko Järvenpää, Aki Vehtari, Pekka Marttinen

Amortized Bayesian Optimization over Discrete Spaces

Kevin Swersky, Yulia Rubanova, David Dohan, Kevin Murphy

An Interpretable and Sample Efficient Deep Kernel for Gaussian Process

Yijue Dai, Tianjian Zhang, Zhidi Lin, Feng Yin, Sergios Theodoridis

Verifying Individual Fairness in Machine Learning Models

Philips George John, Deepak Vijaykeerthy, Diptikalyan Saha

Faster algorithms for Markov equivalence

Zhongyi Hu, Robin Evans

Complex Markov Logic Networks: Expressivity and Liftability

Ondrej Kuzelka

Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks

Meet Vadera, Brian Jalaian, Benjamin Marlin

Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful

Jingge Zhu

Slice Sampling for General Completely Random Measures

Peiyuan Zhu, Alexandre Bouchard-côté, Trevor Campbell

MaskAAE: Latent space optimization for Adversarial Auto-Encoders

Arnab Mondal, Sankalan Pal Chowdhury, Aravind Jayendran, Himanshu Asnani, Parag Singla

Mixed-Membership Stochastic Block Models for Weighted Networks

Adrien Dulac, Eric Gaussier, Christine Largeron

Selling Data at an Auction under Privacy Constraints

Mengxiao Zhang, Fernando Beltran, Jiamou Liu

Robust contrastive learning and nonlinear ICA in the presence of outliers

Hiroaki Sasaki, Takashi Takenouchi, Ricardo Monti, Aapo Hyvärinen

Semi-supervised Sequential Generative Models

Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood

Differentially Private Small Dataset Release Using Random Projections

Lovedeep Gondara, Ke Wang

How Private Are Commonly-Used Voting Rules?

Ao Liu, Yun Lu, Lirong Xia, Vassilis Zikas

Anchored Causal Inference in the Presence of Measurement Error

Basil Saeed, Anastasiya Belyaeva, Yuhao Wang, Caroline Uhler

Spectral Methods for Ranking with Scarce Data

Lalit Jain, Anna Gilbert, Umang Varma

The Indian Chefs Process

Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne

Measurement Dependence Inducing Latent Causal Models

Alex Markham, Moritz Grosse-wentrup

A SUPER* Algorithm to Optimize Paper Bidding in Peer Review

Tanner Fiez, Nihar Shah, Lillian Ratliff

Optimal Statistical Hypothesis Testing for Social Choice

Lirong Xia

Towards Threshold Invariant Fair Classification

Mingliang Chen, Min Wu

Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison

Tengyang Xie, Nan Jiang

Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator

Siamak Zamani Dadaneh, Shahin Boluki, Mingzhang Yin, Mingyuan Zhou, Xiaoning Qian

Identifying causal effects in maximally oriented partially directed acyclic graphs

Emilija Perkovic

GPIRT: A Gaussian Process Model for Item Response Theory

JBrandon Duck-Mayr, Roman Garnett, Jacob Montgomery

One-Bit Compressed Sensing via One-Shot Hard Thresholding

Jie Shen

Relaxed Multivariate Bernoulli Distribution and Its Applications to Deep Generative Models

Xi Wang, Junming Yin

Streaming Nonlinear Bayesian Tensor Decomposition

Zhimeng Pan, Zheng Wang, Shandian Zhe

Compositional uncertainty in deep Gaussian processes

Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill Campbell

Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect

Priyank Agrawal, Theja Tulabandula

The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks

Sikun Yang, Heinz Koeppl

What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination

Laura Niss, Ambuj Tewari

TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP

Nils Rethmeier, Vageesh Kumar Saxena, Isabelle Augenstein

Unknown mixing times in apprenticeship and reinforcement learning

Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour

Neural Likelihoods via Cumulative Distribution Functions

Pawel Chilinski, Ricardo Silva

High Dimensional Discrete Integration over the Hypergrid

Raj Kumar Maity, Arya Mazumdar, Soumyabrata Pal

Skewness Ranking Optimization for Personalized Recommendation

Chuan-Ju Wang, Yu-Neng Chuang, Chih-Ming Chen, Ming-Feng Tsai