UAI 2024

201 papers

Approximate Kernel Density Estimation under Metric-based Local Differential Privacy

Yi Zhou, Yanhao Wang, Long Teng, Qiang Huang, Cen Chen

Trusted re-weighting for label distribution learning

Zhuoran Zheng, Chen Wu, Yeying Jin, Xiuyi Jia

Exploring High-dimensional Search Space via Voronoi Graph Traversing

Aidong Zhao, Xuyang Zhao, Tianchen Gu, Zhaori Bi, Xinwei Sun

Zhiqiang Zhang, Yongqiang Jiang, Qian Gao, Zhipeng Wang

Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem

Cong Zhang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jing Sun

Decentralized Two-Sided Bandit Learning in Matching Market

YiRui Zhang, Zhixuan Fang

Partial Identification with Proxy of Latent Confoundings via Sum-of-ratios Fractional Programming

Zhiheng Zhang, Xinyan Su

Causally Abstracted Multi-armed Bandits

Fabio Massimo Zennaro, Nicholas Bishop, Joel Dyer, Yorgos Felekis, Anisoara Calinescu

Dirichlet Continual Learning: Tackling Catastrophic Forgetting in NLP

Min Zeng, Haiqin Yang, Wei Xue, Qifeng Liu, Yike Guo

Probabilistic reconciliation of mixed-type hierarchical time series

Lorenzo Zambon, Dario Azzimonti, Nicolò Rubattu, Giorgio Corani

Decentralized Online Learning in General-Sum Stackelberg Games

Yaolong Yu, Haipeng Chen

Offline Reward Perturbation Boosts Distributional Shift in Online RL

Zishun Yu, Siteng Kang, Xinhua Zhang

Domain Adaptation with Cauchy-Schwarz Divergence

Wenzhe Yin, Shujian Yu, Yicong Lin, Jie Liu, Jan-Jakob Sonke

Masking the Unknown: Leveraging Masked Samples for Enhanced Data Augmentation

Xun Yao, Zijian Huang, Xinrong Hu, jie Yang, Yi Guo

On Hardware-efficient Inference in Probabilistic Circuits

Lingyun Yao, Martin Trapp, Jelin Leslin, Gaurav Singh, Peng Zhang

Statistical and Causal Robustness for Causal Null Hypothesis Tests

Junhui Yang, Rohit Bhattacharya, Youjin Lee, Ted Westling

Graph Contrastive Learning under Heterophily via Graph Filters

Wenhan Yang, Baharan Mirzasoleiman

Investigating the Impact of Model Width and Density on Generalization in Presence of Label Noise

Yihao Xue, Kyle Whitecross, Baharan Mirzasoleiman

Functional Wasserstein Variational Policy Optimization

Junyu Xuan, Mengjing Wu, Zihe Liu, Jie Lu

\ensuremathα-Former: Local-Feature-Aware (L-FA) Transformer

Zhi Xu, Bin Sun, Yue Bai, Yun Fu

Base Models for Parabolic Partial Differential Equations

Xingzi Xu, Ali Hasan, Jie Ding, Vahid Tarokh

Pix2Code: Learning to Compose Neural Visual Concepts as Programs

Antonia Wüst, Wolfgang Stammer, Quentin Delfosse, Devendra Singh Dhami, Kristian Kersting

RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction

Songli Wu, Liang Du, Jiaqi Yang, Yuai Wang, Dechuan Zhan

Functional Wasserstein Bridge Inference for Bayesian Deep Learning

Mengjing Wu, Junyu Xuan, Jie Lu

Understanding Pathologies of Deep Heteroskedastic Regression

Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt

GCVR: Reconstruction from Cross-View Enable Sufficient and Robust Graph Contrastive Learning

Qianlong Wen, Zhongyu Ouyang, Chunhui Zhang, Yiyue Qian, Chuxu Zhang

Hidden Population Estimation with Indirect Inference and Auxiliary Information

Justin Weltz, Eric Laber, Alexander Volfovsky

Robust Entropy Search for Safe Efficient Bayesian Optimization

Dorina Weichert, Alexander Kister, Sebastian Houben, Patrick Link, Gunar Ernis

Bounding causal effects with leaky instruments

David Watson, Jordan Penn, Lee Gunderson, Gecia Bravo-Hermsdorff, Afsaneh Mastouri

Stein Random Feature Regression

Houston Warren, Rafael Oliveira, Fabio Ramos

Beyond Dirichlet-based Models: When Bayesian Neural Networks Meet Evidential Deep Learning

Hanjing Wang, Qiang Ji

Bias-aware Boolean Matrix Factorization Using Disentangled Representation Learning

Xiao Wang, Jia Wang, Tong Zhao, Yijie Wang, Nan Zhang

AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop

Jing Wang, Yunfei Teng, Anna Choromanska

Metric Learning from Limited Pairwise Preference Comparisons

Zhi Wang, Geelon So, Ramya Korlakai Vinayak

Pure Exploration in Asynchronous Federated Bandits

Zichen Wang, Chuanhao Li, Chenyu Song, Lianghui Wang, Quanquan Gu

Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States

Ziqiao Wang, Yongyi Mao

Model-Free Robust Reinforcement Learning with Sample Complexity Analysis

Yudan Wang, Shaofeng Zou, Yue Wang

Group Fairness in Predict-Then-Optimize Settings for Restless Bandits

Shresth Verma, Yunfan Zhao, Sanket Shah, Niclas Boehmer, Aparna Taneja

Random Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks

Shyam Venkatasubramanian, Ahmed Aloui, Vahid Tarokh

Efficiently Deciding Algebraic Equivalence of Bow-Free Acyclic Path Diagrams

Thijs van Ommen

Offline Bayesian Aleatoric and Epistemic Uncertainty Quantification and Posterior Value Optimisation in Finite-State MDPs

Filippo Valdettaro, Aldo Faisal

Bayesian Pseudo-Coresets via Contrastive Divergence

Piyush Tiwary, Kumar Shubham, Vivek V. Kashyap, Prathosh A. P.

Fast Reliability Estimation for Neural Networks with Adversarial Attack-Driven Importance Sampling

Karim TIT, Teddy Furon

Localised Natural Causal Learning Algorithms for Weak Consistency Conditions

Kai Teh, Kayvan Sadeghi, Terry Soo

A Homogenization Approach for Gradient-Dominated Stochastic Optimization

Jiyuan Tan, Chenyu Xue, Chuwen Zhang, Qi Deng, DongDong Ge

Multi-layer random features and the approximation power of neural networks

Rustem Takhanov

Computing Low-Entropy Couplings for Large-Support Distributions

Samuel Sokota, Dylan Sam, Christian Schroeder de Witt, Spencer Compton, Jakob Foerster

Memorization Capacity for Additive Fine-Tuning with Small ReLU Networks

Jy-yong Sohn, Dohyun Kwon, Seoyeon An, Kangwook Lee

Bayesian Active Learning in the Presence of Nuisance Parameters

Sabina J. Sloman, Ayush Bharti, Julien Martinelli, Samuel Kaski

BanditQ:Fair Bandits with Guaranteed Rewards

Abhishek Sinha

Response Time Improves Gaussian Process Models for Perception and Preferences

Michael Shvartsman, Benjamin Letham, Eytan Bakshy, Stephen Keeley

Cooperative Meta-Learning with Gradient Augmentation

Jongyun Shin, Seungjin Han, Jangho Kim

Unsupervised Feature Selection towards Pattern Discrimination Power

Wangduk Seo, Jaesung Lee

Label-wise Aleatoric and Epistemic Uncertainty Quantification

Yusuf Sale, Paul Hofman, Timo Löhr, Lisa Wimmer, Thomas Nagler

A Graph Theoretic Approach for Preference Learning with Feature Information

Aadirupa Saha, Arun Rajkumar

Anomaly Detection with Variance Stabilized Density Estimation

Amit Rozner, Barak Battash, Henry Li, Lior Wolf, Ofir Lindenbaum

To smooth a cloud or to pin it down: Expressiveness guarantees and insights on score matching in denoising diffusion models

Teodora Reu, Francisco Vargas, Anna Kerekes, Michael Bronstein

Decision-Focused Evaluation of Worst-Case Distribution Shift

Kevin Ren, Yewon Byun, Bryan Wilder

Performative Reinforcement Learning in Gradually Shifting Environments

Ben Rank, Stelios Triantafyllou, Debmalya Mandal, Goran Radanovic

Graph Feedback Bandits with Similar Arms

Han Qi, Guo Fei, Li Zhu

$χ$SPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains

Harsh Poonia, Moritz Willig, Zhongjie Yu, Matej Ze\vcević, Kristian Kersting

Neural Optimal Transport with Lagrangian Costs

Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, Brandon Amos

DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distribution

Matı́as Pizarro, Dorothea Kolossa, Asja Fisher

Zero Inflation as a Missing Data Problem: a Proxy-based Approach

Trung Phung, Jaron Lee, Opeyemi Oladapo-Shittu, Eili Klein, Ayse Gurses

Non-stationary Domain Generalization: Theory and Algorithm

Thai-Hoang Pham, Xueru Zhang, Ping Zhang

Value-Based Abstraction Functions for Abstraction Sampling

Bobak Pezeshki, Kalev Kask, Alexander Ihler, Rina Dechter

Quantifying Representation Reliability in Self-Supervised Learning Models

Young-Jin Park, Hao Wang, Shervin Ardeshir, Navid Azizan

Finite-Time Analysis of Three-Timescale Constrained Actor-Critic and Constrained Natural Actor-Critic Algorithms.

Prashansa Panda, Shalabh Bhatnagar

Cold-start Recommendation by Personalized Embedding Region Elicitation

Hieu Trung Nguyen, Duy Nguyen, Khoa Doan, Viet Anh Nguyen

A Generalized Bayesian Approach to Distribution-on-Distribution Regression

Tin Lok James Ng

Extremely Greedy Equivalence Search

Achille Nazaret, David Blei

Neural Architecture Search Finds Robust Models by Knowledge Distillation

Utkarsh Nath, Yancheng Wang, Yingzhen Yang

Efficient Interactive Maximization of BP and Weakly Submodular Objectives

Adhyyan Narang, Omid Sadeghi, Lillian Ratliff, Maryam Fazel, Jeff Bilmes

General Markov Model for Solving Patrolling Games

Andrzej Nagorko, Marcin Waniek, Małgorzata Róg, Michał Godziszewski, Barbara Rosiak

Partial identification of the maximum mean discrepancy with mismeasured data

Ron Nafshi, Maggie Makar

Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes

Sang Bin Moon, Abolfazl Hashemi

Evaluating Bayesian deep learning for radio galaxy classification

Devina Mohan, Anna M. M. Scaife

Approximation Algorithms for Observer Aware MDPs

Shuwa Miura, Olivier Buffet, Shlomo Zilberstein

Characterising Interventions in Causal Games

Manuj Mishra, James Fox, Michael Wooldridge

Invariant Causal Prediction with Local Models

Alexander Mey, Rui M. Castro

Quantization of Large Language Models with an Overdetermined Basis

Daniil Merkulov, Daria Cherniuk, Alexander Rudikov, Ivan Oseledets, Ekaterina Muravleva

Knowledge Intensive Learning of Credal Networks

Saurabh Mathur, Alessandro Antonucci, Sriraam Natarajan

Learning Causal Abstractions of Linear Structural Causal Models

Riccardo Massidda, Sara Magliacane, Davide Bacciu

Identifying Homogeneous and Interpretable Groups for Conformal Prediction

Natalia Martinez Gil, Dhaval Patel, Chandra Reddy, Giri Ganapavarapu, Roman Vaculin

Learning relevant contextual variables within Bayesian optimization

Julien Martinelli, Ayush Bharti, Armi Tiihonen, S. T. John, Louis Filstroff

Amortized Variational Inference: When and Why?

Charles C. Margossian, David M. Blei

BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts

Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Antonio Vergari, Andrea Passerini

Identifying Causal Changes Between Linear Structural Equation Models

Vineet Malik, Kevin Bello, Asish Ghoshal, Jean Honorio

Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs

Jacqueline Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Kyra Gan

Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance

Jorge Loria, Anindya Bhadra

Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models

Xinyang Liu, Dongsheng Wang, Bowei Fang, Miaoge Li, Yishi Xu

Hybrid CtrlFormer: Learning Adaptive Search Space Partition for Hybrid Action Control via Transformer-based Monte Carlo Tree Search

Jiashun Liu, Xiaotian Hao, Jianye Hao, YAN ZHENG, Yujing Hu

Transductive and Inductive Outlier Detection with Robust Autoencoders

Ofir Lindenbaum, Yariv Aizenbud, Yuval Kluger

Optimizing Language Models for Human Preferences is a Causal Inference Problem

Victoria Lin, Eli Ben-Michael, Louis-Philippe Morency

Learning from Crowds with Dual-View K-Nearest Neighbor

Jiao Li, Liangxiao Jiang, Xue Wu, Wenjun Zhang

Label Consistency-based Worker Filtering for Crowdsourcing

Jiao Li, Liangxiao Jiang, Chaoqun Li, Wenjun Zhang

On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms

Haoyu LEI, Amin Gohari, Farzan Farnia

A General Identification Algorithm For Data Fusion Problems Under Systematic Selection

Jaron Jia Rong Lee, AmirEmad Ghassami, Ilya Shpitser

Support Recovery in Sparse PCA with General Missing Data

Hanbyul Lee, Qifan Song, Jean Honorio

Common Event Tethering to Improve Prediction of Rare Clinical Events

Quinn Lanners, Qin Weng, Marie-Louise Meng, Matthew M. Engelhard