UAI 2021

205 papers

A Bayesian Nonparametric Conditional Two-Sample Test with an Application to Local Causal Discovery Philip A. Boeken, Joris M. Mooij

A Decentralized Policy Gradient Approach to Multi-Task Reinforcement Learning Sihan Zeng, Malik Aqeel Anwar, Thinh T. Doan, Arijit Raychowdhury, Justin Romberg

A Heuristic for Statistical Seriation Komal Dhull, Jingyan Wang, Nihar B. Shah, Yuanzhi Li, R. Ravi

A Kernel Two-Sample Test with Selection Bias Alexis Bellot, Mihaela Schaar

A Nonmyopic Approach to Cost-Constrained Bayesian Optimization Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias Seeger

A Unifying Framework for Observer-Aware Planning and Its Complexity Shuwa Miura, Shlomo Zilberstein

A Variational Approximation for Analyzing the Dynamics of Panel Data Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant, Sathya Ravi, Vikas Singh

A Weaker Faithfulness Assumption Based on Triple Interactions Alexander Marx, Arthur Gretton, Joris M. Mooij

Action Redundancy in Reinforcement Learning Nir Baram, Guy Tennenholtz, Shie Mannor

Active Multi-Fidelity Bayesian Online Changepoint Detection Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams

Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm Kirtan Padh, Diego Antognini, Emma Lejal-Glaude, Boi Faltings, Claudiu Musat

An Optimization and Generalization Analysis for Max-Pooling Networks Alon Brutzkus, Amir Globerson

An Unsupervised Video Game Playstyle Metric via State Discretization Chiu-Chou Lin, Wei-Chen Chiu, I-Chen Wu

Application of Kernel Hypothesis Testing on Set-Valued Data Alexis Bellot, Mihaela Schaar

Approximate Implication with D-Separation Batya Kenig

Approximation Algorithm for Submodular Maximization Under Submodular Cover Naoto Ohsaka, Tatsuya Matsuoka

Asynchronous $ε$-Greedy Bayesian Optimisation George De Ath, Richard M. Everson, Jonathan E. Fieldsend

Bandits with Partially Observable Confounded Data Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni

Bayesian Optimization for Modular Black-Box Systems with Switching Costs Chi-Heng Lin, Joseph D. Miano, Eva L. Dyer

Bayesian Streaming Sparse Tucker Decomposition Shikai Fang, Robert M. Kirby, Shandian Zhe

BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations Xingyu Zhao, Wei Huang, Xiaowei Huang, Valentin Robu, David Flynn

Bias-Corrected Peaks-over-Threshold Estimation of the CVaR Dylan Troop, Frédéric Godin, Jia Yuan Yu

Causal Additive Models with Unobserved Variables Takashi Nicholas Maeda, Shohei Shimizu

Causal and Interventional Markov Boundaries Sofia Triantafillou, Fattaneh Jabbari, Gregory F. Cooper

Certification of Iterative Predictions in Bayesian Neural Networks Matthew Wicker, Luca Laurenti, Andrea Patane, Nicola Paoletti, Alessandro Abate, Marta Kwiatkowska

CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks Dexun Li, Meghna Lowalekar, Pradeep Varakantham

Class Balancing GAN with a Classifier in the Loop Harsh Rangwani, Konda Reddy Mopuri, R. Venkatesh Babu

Classification with Abstention but Without Disparities Nicolas Schreuder, Evgenii Chzhen

Combinatorial Semi-Bandit in the Non-Stationary Environment Wei Chen, Liwei Wang, Haoyu Zhao, Kai Zheng

Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes Will Tebbutt, Arno Solin, Richard E. Turner

Communication Efficient Parallel Reinforcement Learning Mridul Agarwal, Bhargav Ganguly, Vaneet Aggarwal

Competitive Policy Optimization Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar

Compositional Abstraction Error and a Category of Causal Models Eigil F. Rischel, Sebastian Weichwald

Condition Number Bounds for Causal Inference Spencer L. Gordon, Vinayak M. Kumar, Leonard J. Schulman, Piyush Srivastava

Conditionally Independent Data Generation Kartik Ahuja, Prasanna Sattigeri, Karthikeyan Shanmugam, Dennis Wei, Karthikeyan Natesan Ramamurthy, Murat Kocaoglu

Confidence in Causal Discovery with Linear Causal Models David Strieder, Tobias Freidling, Stefan Haffner, Mathias Drton

Constrained Differentially Private Federated Learning for Low-Bandwidth Devices Raouf Kerkouche, Gergely Ács, Claude Castelluccia, Pierre Genevès

Constrained Labeling for Weakly Supervised Learning Chidubem Arachie, Bert Huang

Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee

Contingency-Aware Influence Maximization: A Reinforcement Learning Approach Haipeng Chen, Wei Qiu, Han-Ching Ou, Bo An, Milind Tambe

Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning Yizhao Gao, Nanyi Fei, Guangzhen Liu, Zhiwu Lu, Tao Xiang

Convergence Behavior of Belief Propagation: Estimating Regions of Attraction via Lyapunov Functions Harald Leisenberger, Christian Knoll, Richard Seeber, Franz Pernkopf

CORe: Capitalizing on Rewards in Bandit Exploration Nan Wang, Branislav Kveton, Maryam Karimzadehgan

Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks Adrià Garriga-Alonso, Mark Wilk

Decentralized Multi-Agent Active Search for Sparse Signals Ramina Ghods, Arundhati Banerjee, Jeff Schneider

Deep Kernels with Probabilistic Embeddings for Small-Data Learning Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, T. Yong-Jin Han

Defending SVMs Against Poisoning Attacks: The Hardness and DBSCAN Approach Hu Ding, Fan Yang, Jiawei Huang

Dependency in DAG Models with Hidden Variables Robin J. Evans

Diagnostics for Conditional Density Models and Bayesian Inference Algorithms David Zhao, Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee

Dimension Reduction for Data with Heterogeneous Missingness Yurong Ling, Zijing Liu, Jing-Hao Xue

Disentangling Mixtures of Unknown Causal Interventions Abhinav Kumar, Gaurav Sinha

Distribution-Free Uncertainty Quantification for Classification Under Label Shift Aleksandr Podkopaev, Aaditya Ramdas

Doubly Non-Central Beta Matrix Factorization for DNA Methylation Data Aaron Schein, Anjali Nagulpally, Hanna Wallach, Patrick Flaherty

Dynamic Visualization for L1 Fusion Convex Clustering in Near-Linear Time Bingyuan Zhang, Jie Chen, Yoshikazu Terada

Efficient Debiased Evidence Estimation by Multilevel Monte Carlo Sampling Kei Ishikawa, Takashi Goda

Efficient Greedy Coordinate Descent via Variable Partitioning Huang Fang, Guanhua Fang, Tan Yu, Ping Li

Efficient Online Inference for Nonparametric Mixture Models Rylan Schaeffer, Blake Bordelon, Mikail Khona, Weiwei Pan, Ila Rani Fiete

Enabling Long-Range Exploration in Minimization of Multimodal Functions Jiaxin Zhang, Hoang Tran, Dan Lu, Guannan Zhang

Entropic Inequality Constraints from E-Separation Relations in Directed Acyclic Graphs with Hidden Variables Noam Finkelstein, Beata Zjawin, Elie Wolfe, Ilya Shpitser, Robert W. Spekkens

Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization Jyun-Li Lin, Wei Hung, Shang-Hsuan Yang, Ping-Chun Hsieh, Xi Liu

Estimating Treatment Effects with Observed Confounders and Mediators Shantanu Gupta, Zachary C. Lipton, David Childers

Exact and Approximate Hierarchical Clustering Using A* Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum

Explaining Fast Improvement in Online Imitation Learning Xinyan Yan, Byron Boots, Ching-An Cheng

Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification Yu Wang, Yuesong Shen, Daniel Cremers

Exploring the Loss Landscape in Neural Architecture Search Colin White, Sam Nolen, Yash Savani

Extendability of Causal Graphical Models: Algorithms and Computational Complexity Marcel Wienöbst, Max Bannach, Maciej Liśkiewicz

Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling Difan Zou, Pan Xu, Quanquan Gu

Faster Lifting for Two-Variable Logic Using Cell Graphs Timothy Bremen, Ondřej Kuželka

Featurized Density Ratio Estimation Kristy Choi, Madeline Liao, Stefano Ermon

Federated Stochastic Gradient Langevin Dynamics Khaoula Mekkaoui, Diego Mesquita, Paul Blomstedt, Samuel Kaski

Finite-Time Theory for Momentum Q-Learning Weng Bowen, Xiong Huaqing, Zhao Lin, Liang Yingbin, Zhang Wei

FlexAE: Flexibly Learning Latent Priors for Wasserstein Auto-Encoders Arnab Kumar Mondal, Himanshu Asnani, Parag Singla, Ap Prathosh

Formal Verification of Neural Networks for Safety-Critical Tasks in Deep Reinforcement Learning Davide Corsi, Enrico Marchesini, Alessandro Farinelli

Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting Iwona Hawryluk, Henrique Hoeltgebaum, Swapnil Mishra, Xenia Miscouridou, Ricardo P Schnekenberg, Charles Whittaker, Michaela Vollmer, Seth Flaxman, Samir Bhatt, Thomas A. Mellan

Generalization Error Bounds for Deep Unfolding RNNs Boris Joukovsky, Tanmoy Mukherjee, Huynh Van Luong, Nikos Deligiannis

Generalized Parametric Path Problems Kshitij Gajjar, Girish Varma, Prerona Chatterjee, Jaikumar Radhakrishnan

Generating Adversarial Examples with Graph Neural Networks Florian Jaeckle, M. Pawan Kumar

Generative Archimedean Copulas Yuting Ng, Ali Hasan, Khalil Elkhalil, Vahid Tarokh

Geometric Rates of Convergence for Kernel-Based Sampling Algorithms Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney

Global Explanations with Decision Rules: A Co-Learning Approach Géraldin Nanfack, Paul Temple, Benoît Frénay

GP-ConvCNP: Better Generalization for Conditional Convolutional Neural Processes on Time Series Data Jens Petersen, Gregor Köhler, David Zimmerer, Fabian Isensee, Paul F. Jäger, Klaus H. Maier-Hein

Gradient-Based Optimization for Multi-Resource Spatial Coverage Problems Nitin Kamra, Yan Liu

Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks Jurijs Nazarovs, Ronak R. Mehta, Vishnu Suresh Lokhande, Vikas Singh

Graph-Based Semi-Supervised Learning Through the Lens of Safety Shreyas Sheshadri, Avirup Saha, Priyank Patel, Samik Datta, Niloy Ganguly

Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning Samuel Kessler, Vu Nguyen, Stefan Zohren, Stephen J. Roberts

Hierarchical Infinite Relational Model Feras A. Saad, Vikash K. Mansinghka

Hierarchical Learning of Hidden Markov Models with Clustering Regularization Hui Lan, Antoni B. Chan

Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation Simon Luo, Lamiae Azizi, Mahito Sugiyama

High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces David Eriksson, Martin Jankowiak

Identifying Regions of Trusted Predictions Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner

Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Bjoern Eskofier

Improved Generalization Bounds of Group Invariant / Equivariant Deep Networks via Quotient Feature Spaces Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano

Improving Approximate Optimal Transport Distances Using Quantization Gaspard Beugnot, Aude Genevay, Kristjan Greenewald, Justin Solomon

Improving Uncertainty Calibration of Deep Neural Networks via Truth Discovery and Geometric Optimization Chunwei Ma, Ziyun Huang, Jiayi Xian, Mingchen Gao, Jinhui Xu

Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation Takeshi Teshima, Masashi Sugiyama

Inference of Causal Effects When Control Variables Are Unknown Ludvig Hult, Dave Zachariah

Information Theoretic Meta Learning with Gaussian Processes Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos, Alexandre Galashov

Integer Programming-Based Error-Correcting Output Code Design for Robust Classification Samarth Gupta, Saurabh Amin

Invariant Representation Learning for Treatment Effect Estimation Claudia Shi, Victor Veitch, David M. Blei

Investigating Vulnerabilities of Deep Neural Policies Ezgi Korkmaz

Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection Dennis Ulmer, Giovanni Cinà

Known Unknowns: Learning Novel Concepts Using Reasoning-by-Elimination Harsh Agrawal, Eli A. Meirom, Yuval Atzmon, Shie Mannor, Gal Chechik

Learnable Uncertainty Under Laplace Approximations Agustinus Kristiadi, Matthias Hein, Philipp Hennig

Learning and Certification Under Instance-Targeted Poisoning Ji Gao, Amin Karbasi, Mohammad Mahmoody

Learning in Multi-Player Stochastic Games William Brown

Learning Probabilistic Sentential Decision Diagrams Under Logic Constraints by Sampling and Averaging Renato Lui Geh, Denis Deratani Mauá

Learning Proposals for Probabilistic Programs with Inference Combinators Sam Stites, Heiko Zimmermann, Hao Wu, Eli Sennesh, Jan-Willem Meent

Learning to Learn with Gaussian Processes Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression Zhongjie Yu, Mingye Zhu, Martin Trapp, Arseny Skryagin, Kristian Kersting

Lifted Reasoning Meets Weighted Model Integration Jonathan Feldstein, Vaishak Belle

Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice David S. Watson, Limor Gultchin, Ankur Taly, Luciano Floridi

LocalNewton: Reducing Communication Rounds for Distributed Learning Vipul Gupta, Avishek Ghosh, Michał Dereziński, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney

Markov Equivalence of Max-Linear Bayesian Networks Carlos Améndola, Benjamin Hollering, Seth Sullivant, Ngoc Tran

Matrix Games with Bandit Feedback Brendan O’Donoghue, Tor Lattimore, Ian Osband

Maximal Ancestral Graph Structure Learning via Exact Search Kari Rantanen, Antti Hyttinen, Matti Järvisalo

Measuring Data Leakage in Machine-Learning Models with Fisher Information Awni Hannun, Chuan Guo, Laurens Maaten

Min/max Stability and Box Distributions Michael Boratko, Javier Burroni, Shib Sankar Dasgupta, Andrew McCallum

Minimax Sample Complexity for Turn-Based Stochastic Game Qiwen Cui, Lin F. Yang

Mixed Variable Bayesian Optimization with Frequency Modulated Kernels Changyong Oh, Efstratios Gavves, Max Welling

Modeling Financial Uncertainty with Multivariate Temporal Entropy-Based Curriculums Ramit Sawhney, Arnav Wadhwa, Ayush Mangal, Vivek Mittal, Shivam Agarwal, Rajiv Ratn Shah

Most: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning Tuan Nguyen, Trung Le, He Zhao, Quan Hung Tran, Truyen Nguyen, Dinh Phung

Multi-Output Gaussian Processes for Uncertainty-Aware Recommender Systems Yinchong Yang, Florian Buettner

Multi-Task and Meta-Learning with Sparse Linear Bandits Leonardo Cella, Massimiliano Pontil

Natural Language Adversarial Defense Through Synonym Encoding Xiaosen Wang, Jin Hao, Yichen Yang, Kun He

Nearest Neighbor Search Under Uncertainty Blake Mason, Ardhendu Tripathy, Robert Nowak

Neural Markov Logic Networks Giuseppe Marra, Ondřej Kuželka

No-Regret Approximate Inference via Bayesian Optimisation Rafael Oliveira, Lionel Ott, Fabio Ramos

No-Regret Learning with High-Probability in Adversarial Markov Decision Processes Mahsa Ghasemi, Abolfazl Hashemi, Haris Vikalo, Ufuk Topcu

Non-PSD Matrix Sketching with Applications to Regression and Optimization Zhili Feng, Fred Roosta, David P. Woodruff

NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation Xiaohui Zeng, Raquel Urtasun, Richard Zemel, Sanja Fidler, Renjie Liao

On Random Kernels of Residual Architectures Etai Littwin, Tomer Galanti, Lior Wolf

On the Distribution of Penultimate Activations of Classification Networks Minkyo Seo, Yoonho Lee, Suha Kwak

On the Distributional Properties of Adaptive Gradients Zhiyi Zhang, Ziyin Liu

On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues

Optimized Auxiliary Particle Filters: Adapting Mixture Proposals via Convex Optimization Nicola Branchini, Víctor Elvira

PALM: Probabilistic Area Loss Minimization for Protein Sequence Alignment Fan Ding, Nan Jiang, Jianzhu Ma, Jian Peng, Jinbo Xu, Yexiang Xue

Partial Identifiability in Discrete Data with Measurement Error Noam Finkelstein, Roy Adams, Suchi Saria, Ilya Shpitser

Path Dependent Structural Equation Models Ranjani Srinivasan, Jaron J. R. Lee, Rohit Bhattacharya, Ilya Shpitser

Path-BN: Towards Effective Batch Normalization in the Path Space for ReLU Networks Xufang Luo, Qi Meng, Wei Chen, Yunhong Wang, Tie-Yan Liu

PLSO: A Generative Framework for Decomposing Nonstationary Time-Series into Piecewise Stationary Oscillatory Components Andrew H. Song, Demba Ba, Emery N. Brown

Possibilistic Preference Elicitation by Minimax Regret Loïc Adam, Sebastien Destercke

Post-Hoc Loss-Calibration for Bayesian Neural Networks Meet P. Vadera, Soumya Ghosh, Kenney Ng, Benjamin M. Marlin

Principal Component Analysis in the Stochastic Differential Privacy Model Fanhua Shang, Zhihui Zhang, Tao Xu, Yuanyuan Liu, Hongying Liu

Probabilistic DAG Search Julia Grosse, Cheng Zhang, Philipp Hennig

Probabilistic Selection of Inducing Points in Sparse Gaussian Processes Anders Kirk Uhrenholt, Valentin Charvet, Bjørn Sand Jensen

Probabilistic Task Modelling for Meta-Learning Cuong C. Nguyen, Thanh-Toan Do, Gustavo Carneiro

PROVIDE: A Probabilistic Framework for Unsupervised Video Decomposition Polina Zablotskaia, Edoardo A. Dominici, Leonid Sigal, Andreas M. Lehrmann

pRSL: Interpretable Multi-Label Stacking by Learning Probabilistic Rules Michael Kirchhof, Lena Schmid, Christopher Reining, Michael Hompel, Markus Pauly

Q-Paths: Generalizing the Geometric Annealing Path Using Power Means Vaden Masrani, Rob Brekelmans, Thang Bui, Frank Nielsen, Aram Galstyan, Greg Ver Steeg, Frank Wood

Random Probabilistic Circuits Nicola Di Mauro, Gennaro Gala, Marco Iannotta, Teresa M.A. Basile

Regstar: Efficient Strategy Synthesis for Adversarial Patrolling Games David Klaška, Antonín Kučera, Vít Musil, Vojtěch Řehák

ReZero Is All You Need: Fast Convergence at Large Depth Thomas Bachlechner, Bodhisattwa Prasad Majumder, Henry Mao, Gary Cottrell, Julian McAuley

RISAN: Robust Instance Specific Deep Abstention Network Bhavya Kalra, Kulin Shah, Naresh Manwani

Robust Principal Component Analysis for Generalized Multi-View Models Frank Nussbaum, Joachim Giesen

Robust Reinforcement Learning Under Minimax Regret for Green Security Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, Milind Tambe

Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting Adam D. Cobb, Brian Jalaian

SDM-Net: A Simple and Effective Model for Generalized Zero-Shot Learning Shabnam Daghaghi, Tharun Medini, Anshumali Shrivastava

Sequential Core-Set Monte Carlo Boyan Beronov, Christian Weilbach, Frank Wood, Trevor Campbell

SGD with Low-Dimensional Gradients with Applications to Private and Distributed Learning Shiva Prasad Kasiviswanathan

Similarity Measure for Sparse Time Course Data Based on Gaussian Processes Zijing Liu, Mauricio Barahona

Simple Combinatorial Algorithms for Combinatorial Bandits: Corruptions and Approximations Haike Xu, Jian Li

Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks Apoorva Sharma, Navid Azizan, Marco Pavone

Sparse Linear Networks with a Fixed Butterfly Structure: Theory and Practice Nir Ailon, Omer Leibovitch, Vineet Nair

Statistical Mechanical Analysis of Neural Network Pruning Rupam Acharyya, Ankani Chattoraj, Boyu Zhang, Shouman Das, Daniel Štefankovič

Statistically Robust Neural Network Classification Benjie Wang, Stefan Webb, Tom Rainforth

Staying in Shape: Learning Invariant Shape Representations Using Contrastive Learning Jeffrey Gu, Serena Yeung

Stochastic Continuous Normalizing Flows: Training SDEs as ODEs Liam Hodgkinson, Chris Heide, Fred Roosta, Michael W. Mahoney

Stochastic Model for Sunk Cost Bias Jon Kleinberg, Sigal Oren, Manish Raghavan, Nadav Sklar

Strategically Efficient Exploration in Competitive Multi-Agent Reinforcement Learning Robert Loftin, Aadirupa Saha, Sam Devlin, Katja Hofmann

Structured Sparsification with Joint Optimization of Group Convolution and Channel Shuffle Xin-Yu Zhang, Kai Zhao, Taihong Xiao, Ming-Ming Cheng, Ming-Hsuan Yang

Subseasonal Climate Prediction in the Western US Using Bayesian Spatial Models Vishwak Srinivasan, Justin Khim, Arindam Banerjee, Pradeep Ravikumar

Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan

Sum-Product Laws and Efficient Algorithms for Imprecise Markov Chains Jasper De Bock, Alexander Erreygers, Thomas Krak

Symmetric Wasserstein Autoencoders Sun Sun, Hongyu Guo

Task Similarity Aware Meta Learning: Theory-Inspired Improvement on MAML Pan Zhou, Yingtian Zou, Xiao-Tong Yuan, Jiashi Feng, Caiming Xiong, Steven Hoi

Tensor-Train Density Estimation Georgii S. Novikov, Maxim E. Panov, Ivan V. Oseledets

Testification of Condorcet Winners in Dueling Bandits Björn Haddenhorst, Viktor Bengs, Jasmin Brandt, Eyke Hüllermeier

The Complexity of Nonconvex-Strongly-Concave Minimax Optimization Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He

The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization Yifei Min, Lin Chen, Amin Karbasi

The Neural Moving Average Model for Scalable Variational Inference of State Space Models Thomas Ryder, Dennis Prangle, Andrew Golightly, Isaac Matthews

The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober, Carl E. Rasmussen, Mark Wilk

Thompson Sampling for Markov Games with Piecewise Stationary Opponent Policies Anthony DiGiovanni, Ambuj Tewari

Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms Fengxiang He, Bohan Wang, Dacheng Tao

Time-Variant Variational Transfer for Value Functions Giuseppe Canonaco, Andrea Soprani, Matteo Giuliani, Andrea Castelletti, Manuel Roveri, Marcello Restelli

Towards a Unified Framework for Fair and Stable Graph Representation Learning Chirag Agarwal, Himabindu Lakkaraju, Marinka Zitnik

Towards Robust Episodic Meta-Learning Beyza Ermis, Giovanni Zappella, Cédric Archambeau

Towards Tractable Optimism in Model-Based Reinforcement Learning Aldo Pacchiano, Philip Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts

Tractable Computation of Expected Kernels Wenzhe Li, Zhe Zeng, Antonio Vergari, Guy Broeck

TreeBERT: A Tree-Based Pre-Trained Model for Programming Language Xue Jiang, Zhuoran Zheng, Chen Lyu, Liang Li, Lei Lyu

Trumpets: Injective Flows for Inference and Inverse Problems Konik Kothari, AmirEhsan Khorashadizadeh, Maarten Hoop, Ivan Dokmanić

Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet

Unbiased Gradient Estimation for Variational Auto-Encoders Using Coupled Markov Chains Francisco J. R. Ruiz, Michalis K. Titsias, Taylan Cemgil, Arnaud Doucet

Uncertainty in Minimum Cost Multicuts for Image and Motion Segmentation Amirhossein Kardoost, Margret Keuper

Uncertainty-Aware Sensitivity Analysis Using Rényi Divergences Topi Paananen, Michael Riis Andersen, Aki Vehtari

Unsupervised Anomaly Detection with Adversarial Mirrored Autoencoders Gowthami Somepalli, Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi

Unsupervised Constrained Community Detection via Self-Expressive Graph Neural Network Sambaran Bandyopadhyay, Vishal Peter

Unsupervised Program Synthesis for Images by Sampling Without Replacement Chenghui Zhou, Chun-Liang Li, Barnabás Póczos

Variance Reduction in Frequency Estimators via Control Variates Method Rameshwar Pratap, Raghav Kulkarni

Variance-Dependent Best Arm Identification Pinyan Lu, Chao Tao, Xiaojin Zhang

Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David Blei, Itsik Pe’er

Variational Inference with Continuously-Indexed Normalizing Flows Anthony Caterini, Rob Cornish, Dino Sejdinovic, Arnaud Doucet

Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence Ghassen Jerfel, Serena Wang, Clara Wong-Fannjiang, Katherine A. Heller, Yian Ma, Michael I. Jordan

Weighted Model Counting with Conditional Weights for Bayesian Networks Paulius Dilkas, Vaishak Belle

When Is Particle Filtering Efficient for Planning in Partially Observed Linear Dynamical Systems? Simon S. Du, Wei Hu, Zhiyuan Li, Ruoqi Shen, Zhao Song, Jiajun Wu

XOR-SGD: Provable Convex Stochastic Optimization for Decision-Making Under Uncertainty Fan Ding, Yexiang Xue