UAI 2024

201 papers

Quantifying Local Model Validity using Active Learning

Sven Lämmle, Can Bogoclu, Robert Vosshall, Anselm Haselhoff, Dirk Roos

DataSP: A Differential All-to-All Shortest Path Algorithm for Learning Costs and Predicting Paths with Context

Alan Lahoud, Erik Schaffernicht, Johannes Stork

Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction

Yunhyeok Kwak, Inwoo Hwang, Dooyoung Kim, Sanghack Lee, Byoung-tak Zhang

Optimization Framework for Semi-supervised Attributed Graph Coarsening

Manoj Kumar, Subhanu Halder, Archit Kane, Ruchir Gupta, Sandeep Kumar

Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions

Patrick Kuiper, Ali Hasan, Wenhao Yang, Yuting Ng, Hoda Bidkhori

How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression

Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus

ILP-FORMER: Solving Integer Linear Programming with Sequence to Multi-Label Learning

Shufeng Kong, Caihua Liu, Carla Gomes

Causal Discovery with Deductive Reasoning: One Less Problem

Jonghwan Kim, Inwoo Hwang, Sanghack Lee

Active Learning Framework for Incomplete Networks

Tung Khong, Cong Tran, Cuong Pham

Targeted Reduction of Causal Models

Armin Kekić, Bernhard Schölkopf, Michel Besserve

Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable

Yuta Kawakami, manabu kuroki, Jin Tian

Probabilities of Causation for Continuous and Vector Variables

Yuta Kawakami, manabu kuroki, Jin Tian

Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection

Moussa Kassem-Sbeyti, Michelle Karg, Christian Wirth, Nadja Klein, Sahin Albayrak

Low-rank Matrix Bandits with Heavy-tailed Rewards

Yue Kang, Cho-jui Hsieh, Thomas Chun Man Lee

Towards Scalable Bayesian Transformers: Investigating stochastic subset selection for NLP

Peter Johannes Tejlgaard Kampen, Gustav Ragnar Stoettrup Als, Michael Riis Andersen

Adaptive Softmax Trees for Many-Class Classification

Rasul Kairgeldin, Magzhan Gabidolla, Miguel Carreira-Perpinan

On the Convergence of Hierarchical Federated Learning with Partial Worker Participation

Xiaohan Jiang, Hongbin Zhu

Early-Exit Neural Networks with Nested Prediction Sets

Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric Nalisnick

Equilibrium Computation in Multidimensional Congestion Games: CSP and Learning Dynamics Approaches

Mohammad T. Irfan, Hau Chan, Jared Soundy

Revisiting Convergence of AdaGrad with Relaxed Assumptions

Yusu Hong, Junhong Lin

A Global Markov Property for Solutions of Stochastic Difference Equations and the corresponding Full Time Graphs

Tom Hochsprung, Jakob Runge, Andreas Gerhardus

Sound Heuristic Search Value Iteration for Undiscounted POMDPs with Reachability Objectives

Qi Heng Ho, Martin Feather, Federico Rossi, Zachary Sunberg, Morteza Lahijanian

Recursively-Constrained Partially Observable Markov Decision Processes

Qi Heng Ho, Tyler Becker, Benjamin Kraske, Zakariya Laouar, Martin Feather

Quantum Kernelized Bandits

Yasunari Hikima, Kazunori Murao, Sho Takemori, Yuhei Umeda

Neural Active Learning Meets the Partial Monitoring Framework

Maxime Heuillet, Ola Ahmad, Audrey Durand

On Overcoming Miscalibrated Conversational Priors in LLM-based ChatBots

Christine Herlihy, Jennifer Neville, Tobias Schnabel, Adith Swaminathan

Adjustment Identification Distance: A gadjid for Causal Structure Learning

Leonard Henckel, Theo Würtzen, Sebastian Weichwald

Faster Perfect Sampling of Bayesian Network Structures

Juha Harviainen, Mikko Koivisto

No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processes

Minbiao Han, Fengxue Zhang, Yuxin Chen

Characterizing Data Point Vulnerability as Average-Case Robustness

Tessa Han, Suraj Srinivas, Himabindu Lakkaraju

One Shot Inverse Reinforcement Learning for Stochastic Linear Bandits

Etash Guha, Jim James, Krishna Acharya, Vidya Muthukumar, Ashwin Pananjady

ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-variable Context Encoding

Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata

GeONet: a neural operator for learning the Wasserstein geodesic

Andrew Gracyk, Xiaohui Chen

Bootstrap Your Conversions: Thompson Sampling for Partially Observable Delayed Rewards

Marco Gigli, Fabio Stella

Uncertainty Estimation with Recursive Feature Machines

Daniel Gedon, Amirhesam Abedsoltan, Thomas B. Schön, Mikhail Belkin

Consistency Regularization for Domain Generalization with Logit Attribution Matching

Han Gao, Kaican Li, Weiyan Xie, Zhi Lin, Yongxiang Huang

Guaranteeing Robustness Against Real-World Perturbations In Time Series Classification Using Conformalized Randomized Smoothing

Nicola Franco, Jakob Spiegelberg, Jeanette Miriam Lorenz, Stephan Günnemann

Last-iterate Convergence Separation between Extra-gradient and Optimism in Constrained Periodic Games

Yi Feng, Ping Li, Ioannis Panageas, Xiao Wang

Generalized Expected Utility as a Universal Decision Rule – A Step Forward

Hélène Fargier, Pierre Pomeret-Coquot

Enhancing Patient Recruitment Response in Clinical Trials: an Adaptive Learning Framework

Xinying Fang, Shouhao Zhou

Center-Based Relaxed Learning Against Membership Inference Attacks

Xingli Fang, Jung-Eun Kim

Multi-fidelity Bayesian Optimization with Multiple Information Sources of Input-dependent Fidelity

Mingzhou Fan, Byung-Jun Yoon, Edward Dougherty, Nathan Urban, Francis Alexander

EntProp: High Entropy Propagation for Improving Accuracy and Robustness

Shohei Enomoto

Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions

Mai Elkady, Thu Bui, Bruno Ribeiro, David Inouye

Approximate Bayesian Computation with Path Signatures

Joel Dyer, Patrick Cannon, Sebastian M. Schmon

Bandits with Knapsacks and Predictions

Davide Drago, Andrea Celli, Marek Elias

Learning Distributionally Robust Tractable Probabilistic Models in Continuous Domains

Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi

Online Policy Optimization for Robust Markov Decision Process

Jing Dong, Jingwei Li, Baoxiang Wang, Jingzhao Zhang

End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty

My H. Dinh, James Kotary, Ferdinando Fioretto

Learning to Rank for Active Learning via Multi-Task Bilevel Optimization

Zixin Ding, Si Chen, Ruoxi Jia, Yuxin Chen

Calibrated and Conformal Propensity Scores for Causal Effect Estimation

Shachi Deshpande, Volodymyr Kuleshov

Reflected Schrödinger Bridge for Constrained Generative Modeling

Wei Deng, Yu Chen, Nicole Tianjiao Yang, Hengrong Du, Qi Feng

On Convergence of Federated Averaging Langevin Dynamics

Wei Deng, Qian Zhang, Yian Ma, Zhao Song, Guang Lin

Discrete Probabilistic Inference as Control in Multi-path Environments

Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, Yoshua Bengio

The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data

Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere

Detecting critical treatment effect bias in small subgroups

Piersilvio De Bartolomeis, Javier Abad, Konstantin Donhauser, Fanny Yang

Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring

Khoi Tran Dang, Kevin Delmas, Jérémie Guiochet, Joris Guerin

Linear Opinion Pooling for Uncertainty Quantification on Graphs

Clemens Damke, Eyke Hüllermeier

Power Mean Estimation in Stochastic Monte-Carlo Tree Search

Tuan Dam, Odalric-ambrym Maillard, Emilie Kaufmann

Normalizing Flows for Conformal Regression

Nicolo Colombo

Towards Representation Learning for Weighting Problems in Design-Based Causal Inference

Oscar Clivio, Avi Feller, Chris Holmes

Towards Minimax Optimality of Model-based Robust Reinforcement Learning

Pierre Clavier, Erwan Le Pennec, Matthieu Geist

Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression

Lucas Clarte, Adrien Vandenbroucque, Guillaume Dalle, Bruno Loureiro, Florent Krzakala

Fast Interactive Search under a Scale-Free Comparison Oracle

Daniyar Chumbalov, Lars Klein, Lucas Maystre, Matthias Grossglauser

Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation

Yoichi Chikahara, Kansei Ushiyama

End-to-end Conditional Robust Optimization

Abhilash Reddy Chenreddy, Erick Delage

Inference for Optimal Linear Treatment Regimes in Personalized Decision-making

Yuwen Cheng, Shu Yang

SMuCo: Reinforcement Learning for Visual Control via Sequential Multi-view Total Correlation

Tong Cheng, Hang Dong, Lu Wang, Bo Qiao, Qingwei Lin

Adaptive Time-Stepping Schedules for Diffusion Models

Yuzhu Chen, Fengxiang He, Shi Fu, Xinmei Tian, Dacheng Tao

Conditional Bayesian Quadrature

Zonghao Chen, Masha Naslidnyk, Arthur Gretton, François-Xavier Briol

Gradient descent in matrix factorization: Understanding large initialization

Hengchao Chen, Xin Chen, Mohamad Elmasri, Qiang Sun

Generalization and Learnability in Multiple Instance Regression

Kushal Chauhan, Rishi Saket, Lorne Applebaum, Ashwinkumar Badanidiyuru, Chandan Giri

QuantProb: Generalizing Probabilities along with Predictions for a Pre-trained Classifier

Aditya Challa, Soma Dhavala, Snehanshu Saha

How to Fix a Broken Confidence Estimator: Evaluating Post-hoc Methods for Selective Classification with Deep Neural Networks

Luı́s Felipe Cattelan, Danilo Silva

Multi-Relational Structural Entropy

Yuwei Cao, Hao Peng, Angsheng Li, Chenyu You, Zhifeng Hao

Fair Active Learning in Low-Data Regimes

Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson

Privacy-Aware Randomized Quantization via Linear Programming

Zhongteng Cai, Xueru Zhang, Mohammad Mahdi Khalili

Sample Average Approximation for Black-Box Variational Inference

Javier Burroni, Justin Domke, Daniel Sheldon

Revisiting Kernel Attention with Correlated Gaussian Process Representation

Long Minh Bui, Tho Tran Huu, Duy Dinh, Tan Minh Nguyen, Trong Nghia Hoang

Products, Abstractions and Inclusions of Causal Spaces

Simon Buchholz, Junhyung Park, Bernhard Schölkopf

Polynomial Semantics of Tractable Probabilistic Circuits

Oliver Broadrick, Honghua Zhang, Guy Van den Broeck

Using Autodiff to Estimate Posterior Moments, Marginals and Samples

Sam Bowyer, Thomas Heap, Laurence Aitchison

Publishing Number of Walks and Katz Centrality under Local Differential Privacy

Louis Betzer, Vorapong Suppakitpaisarn, Quentin Hillebrand

Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models

Lucas Berry, Axel Brando, David Meger

MetaCOG: A Heirarchical Probabilistic Model for Learning Meta-Cognitive Visual Representations

Marlene Berke, Zhangir Azerbayev, Mario Belledonne, Zenna Tavares, Julian Jara-Ettinger

Linearly Constrained Gaussian Processes are SkewGPs: application to Monotonic Preference Learning and Desirability

Alessio Benavoli, Dario Azzimonti

Towards Bounding Causal Effects under Markov Equivalence

Alexis Bellot

Learning Accurate and Interpretable Decision Trees

Maria-Florina Balcan, Dravyansh Sharma

Walking the Values in Bayesian Inverse Reinforcement Learning

Ondrej Bajgar, Alessandro Abate, Konstantinos Gatsis, Michael Osborne

Differentially Private No-regret Exploration in Adversarial Markov Decision Processes

Shaojie Bai, Lanting Zeng, Chengcheng Zhao, Xiaoming Duan, Mohammad Sadegh Talebi

Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization

Damiano Azzolini, Fabrizio Riguzzi

Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activations

Mohammad Azizmalayeri, Ameen Abu-Hanna, Giovanni Cinà

On the Capacitated Facility Location Problem with Scarce Resources

Gennaro Auricchio, Harry J. Clough, Jie Zhang

Identifiability of total effects from abstractions of time series causal graphs

Charles K. Assaad, Emilie Devijver, Eric Gaussier, Gregor Goessler, Anouar Meynaoui

FedAST: Federated Asynchronous Simultaneous Training

Baris Askin, PRANAY SHARMA, Carlee Joe-Wong, Gauri Joshi

Latent Representation Entropy Density for Distribution Shift Detection

Fabio Arnez, Daniel Alfonso Montoya Vasquez, Ansgar Radermacher, François Terrier

Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling

Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba

CSS: Contrastive Semantic Similarities for Uncertainty Quantification of LLMs

Shuang Ao, Stefan Rueger, Advaith Siddharthan

Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems

Rafael Anderka, Marc Peter Deisenroth, So Takao

Convergence Behavior of an Adversarial Weak Supervision Method

Steven An, Sanjoy Dasgupta