UAI 2023

243 papers

Probabilistic Multi-Dimensional Classification

Vu-Linh Nguyen, Yang Yang, Cassio de Campos

Simple Transferability Estimation for Regression Tasks

Cuong N. Nguyen, Phong Tran, Lam Si Tung Ho, Vu Dinh, Anh T. Tran

Keep-Alive Caching for the Hawkes process

Sushirdeep Narayana, Ian A. Kash

Two-phase Attacks in Security Games

Andrzej Nagorko, Pawel Ciosmak, Tomasz Michalak

Active metric learning and classification using similarity queries

Namrata Nadagouda, Austin Xu, Mark A. Davenport

On Testability and Goodness of Fit Tests in Missing Data Models

Razieh Nabi, Rohit Bhattacharya

Composing Efficient, Robust Tests for Policy Selection

Dustin Morrill, Thomas J. Walsh, Daniel Hernández, Peter R. Wurman, Peter Stone

Adaptive Conditional Quantile Neural Processes

Peiman Mohseni, Nick Duffield, Bani Mallick, Arman Hasanzadeh

On Minimizing the Impact of Dataset Shifts on Actionable Explanations

Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju

On the Relation between Policy Improvement and Off-Policy Minimum-Variance Policy Evaluation

Alberto Maria Metelli, Samuele Meta, Marcello Restelli

KrADagrad: Kronecker approximation-domination gradient preconditioned stochastic optimization

Jonathan Mei, Alexander Moreno, Luke Walters

Causal information splitting: Engineering proxy features for robustness to distribution shifts

Bijan Mazaheri, Atalanti Mastakouri, Dominik Janzing, Michaela Hardt

TCE: A Test-Based Approach to Measuring Calibration Error

Takuo Matsubara, Niek Tax, Richard Mudd, Ido Guy

Knowledge Intensive Learning of Cutset Networks

Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan

Partial identification of dose responses with hidden confounders

Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan

The Shrinkage-Delinkage Trade-off: an Analysis of Factorized Gaussian Approximations for Variational Inference

Charles C. Margossian, Lawrence K. Saul

Random Reshuffling with Variance Reduction: New Analysis and Better Rates

Grigory Malinovsky, Alibek Sailanbayev, Peter Richtarik

Federated learning of models pre-trained on different features with consensus graphs

Tengfei Ma, Trong Nghia Hoang, Jie Chen

DeepGD3: Unknown-Aware Deep Generative/Discriminative Hybrid Defect Detector for PCB Soldering Inspection

Ching-Wen Ma, Yanwei Liu

Practical privacy-preserving Gaussian process regression via secret sharing

Jinglong Luo, Yehong Zhang, Jiaqi Zhang, Shuang Qin, Hui Wang

Benefits of monotonicity in safe exploration with Gaussian processes

Arpan Losalka, Jonathan Scarlett

No-Regret Linear Bandits beyond Realizability

Chong Liu, Ming Yin, Yu-Xiang Wang

Residual-based error bound for physics-informed neural networks

Shuheng Liu, Xiyue Huang, Pavlos Protopapas

Accelerating Voting by Quantum Computation

Ao Liu, Qishen Han, Lirong Xia, Nengkun Yu

BISCUIT: Causal Representation Learning from Binary Interactions

Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen

CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models

Jiazheng Li, Zhaoyue Sun, Bin Liang, Lin Gui, Yulan He

Gaussian Process Surrogate Models for Neural Networks

Michael Y. Li, Erin Grant, Thomas L. Griffiths

Nonconvex stochastic scaled gradient descent and generalized eigenvector problems

Chris Junchi Li, Michael I Jordan

Memory Mechanism for Unsupervised Anomaly Detection

Jiahao Li, Yiqiang Chen, Yunbing Xing

When are post-hoc conceptual explanations identifiable?

Tobias Leemann, Michael Kirchhof, Yao Rong, Enkelejda Kasneci, Gjergji Kasneci

Finding Invariant Predictors Efficiently via Causal Structure

Kenneth Lee, Md Musfiqur Rahman, Murat Kocaoglu

Towards better certified segmentation via diffusion models

Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon, Marie-Pierre Revel, Siddharth Garg

Variable importance matching for causal inference

Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page

Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances

Anusha Lalitha Lalitha, Kousha Kalantari, Yifei Ma, Anoop Deoras, Branislav Kveton

Optimal Budget Allocation for Crowdsourcing Labels for Graphs

Adithya Kulkarni, Mohna Chakraborty, Sihong Xie, Qi Li

Differentially private synthetic data using KD-trees

Eleonora Kreacic, Navid Nouri, Vamsi K. Potluru, Tucker Balch, Manuela Veloso

Risk-aware curriculum generation for heavy-tailed task distributions

Cevahir Koprulu, Thiago D. Simão, Nils Jansen, Ufuk Topcu

Reward-machine-guided, self-paced reinforcement learning

Cevahir Koprulu, Ufuk Topcu

Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting

Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu

Universal Graph Contrastive Learning with a Novel Laplacian Perturbation

Taewook Ko, Yoonhyuk Choi, Chong-Kwon Kim

Causal effect estimation from observational and interventional data through matrix weighted linear estimators

Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach

On Identifiability of Conditional Causal Effects

Yaroslav Kivva, Jalal Etesami, Negar Kiyavash

Phase-shifted adversarial training

Yeachan Kim, Seongyeon Kim, Ihyeok Seo, Bonggun Shin

How to use dropout correctly on residual networks with batch normalization

Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang, DongGeon Lee, Sang Woo Kim

Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions

Karine Karine, Predrag Klasnja, Susan A. Murphy, Benjamin M. Marlin

Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning

Belhal Karimi, Ping Li, Xiaoyun Li

Heavy-tailed linear bandit with Huber regression

Minhyun Kang, Gi-Soo Kim

Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition

David Kaltenpoth, Jilles Vreeken

Nyström M-Hilbert-Schmidt independence criterion

Florian Kalinke, Zoltan Szabo

Bayesian inference for vertex-series-parallel partial orders

Chuxuan Jiang, Geoff K. Nicholls, Jeong Eun Lee

Multi-view graph contrastive learning for solving vehicle routing problems

Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang

Content Sharing Design for Social Welfare in Networked Disclosure Game

Feiran Jia, Chenxi Qiu, Sarah Rajtmajer, Anna Squicciarini

Incentivising Diffusion while Preserving Differential Privacy

Fengjuan. Jia, Mengxiao. Zhang, Jiamou. Liu, Bakh Khoussainov

Noisy adversarial representation learning for effective and efficient image obfuscation

Jonghu Jeong, Minyong Cho, Philipp Benz, Tae-hoon Kim

Robust statistical comparison of random variables with locally varying scale of measurement

Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann, Thomas Augustin

Investigating a Generalization of Probabilistic Material Implication and Bayesian Conditionals

Michael Jahn, Matthias Scheutz

Posterior sampling-based online learning for the stochastic shortest path model

Mehdi Jafarnia-Jahromi, Liyu Chen, Rahul Jain, Haipeng Luo

A near-optimal high-probability swap-Regret upper bound for multi-agent bandits in unknown general-sum games

Zhiming Huang, Jianping Pan

ASTRA: Understanding the practical impact of robustness for probabilistic programs

Zixin Huang, Saikat Dutta, Sasa Misailovic

Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning

Bingshan Hu, Tianyue H. Zhang, Nidhi Hegde, Mark Schmidt

Increasing effect sizes of pairwise conditional independence tests between random vectors

Tom Hochsprung, Jonas Wahl, Andreas Gerhardus, Urmi Ninad, Jakob Runge

Massively parallel reweighted wake-sleep

Thomas Heap, Gavin Leech, Laurence Aitchison

Scalable and robust tensor ring decomposition for large-scale data

Yicong He, George K. Atia

Loosely consistent emphatic temporal-difference learning

Jiamin He, Fengdi Che, Yi Wan, A. Rupam Mahmood

Inference and sampling of point processes from diffusion excursions

Ali Hasan, Yu Chen, Yuting Ng, Mohamed Abdelghani, Anderson Schneider

On inference and learning with probabilistic generating circuits

Juha Harviainen, Vaidyanathan Peruvemba Ramaswamy, Mikko Koivisto

Revisiting Bayesian network learning with small vertex cover

Juha Harviainen, Mikko Koivisto

On the Convergence of Continual Learning with Adaptive Methods

Seungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee

Differentiable user models

Alex Hämäläinen, Mustafa Mert Çelikok, Samuel Kaski

Interpretable differencing of machine learning models

Swagatam Haldar, Diptikalyan Saha, Dennis Wei, Rahul Nair, Elizabeth M. Daly

Sufficient identification conditions and semiparametric estimation under missing not at random mechanisms

Anna Guo, Jiwei Zhao, Razieh Nabi

Causal Discovery for time series from multiple datasets with latent contexts

Wiebke Günther, Urmi Ninad, Jakob Runge

Functional causal Bayesian optimization

Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa

Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow

Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata

Stochastic Graphical Bandits with Heavy-Tailed Rewards

Yutian Gou, Jinfeng Yi, Lijun Zhang

A scalable Walsh-Hadamard regularizer to overcome the low-degree spectral bias of neural networks

Ali Gorji, Andisheh Amrollahi, Andreas Krause

Piecewise Deterministic Markov Processes for Bayesian Neural Networks

Ethan Goan, Dimitri Perrin, Kerrie Mengersen, Clinton Fookes

Vacant holes for unsupervised detection of the outliers in compact latent representation

Misha Glazunov, Apostolis Zarras

Fast and scalable score-based kernel calibration tests

Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton

Probabilistically robust conformal prediction

Subhankar Ghosh, Yuanjie Shi, Taha Belkhouja, Yan Yan, Jana Doppa

Copula-based deep survival models for dependent censoring

Ali Hossein Foomani Gharari, Michael Cooper, Russell Greiner, Rahul G Krishnan

Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates

Sahra Ghalebikesabi, Chris C. Holmes, Edwin Fong, Brieuc Lehmann

A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models

Sinong Geng, houssam nassif, Carlos A. Manzanares

In- or out-of-distribution detection via dual divergence estimation

Sahil Garg, Sanghamitra Dutta, Mina Dalirrooyfard, Anderson Schneider, Yuriy Nevmyvaka

Information theoretic clustering via divergence maximization among clusters

Sahil Garg, Mina Dalirrooyfard, Anderson Schneider, Yeshaya Adler, Yuriy Nevmyvaka

Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and Prediction

Chengmin Gao, Bin Li

Does Momentum Help in Stochastic Optimization? A Sample Complexity Analysis.

Swetha Ganesh, Rohan Deb, Gugan Thoppe, Amarjit Budhiraja

On the role of model uncertainties in Bayesian optimisation

Jonathan Foldager, Mikkel Jordahn, Lars K. Hansen, Michael R. Andersen

Logit-based ensemble distribution distillation for robust autoregressive sequence uncertainties

Yassir Fathullah, Guoxuan Xia, Mark J. F. Gales

Generating Synthetic Datasets by Interpolating along Generalized Geodesics

Jiaojiao Fan, David Alvarez-Melis

Personalized federated domain adaptation for item-to-item recommendation

Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang

Deep Gaussian mixture ensembles

Yousef El-Laham, Niccolò Dalmasso, Elizabeth Fons, Svitlana Vyetrenko

Studying the Effect of GNN Spatial Convolutions On The Embedding Space’s Geometry

Claire Donnat, So Won Jeong

Neural probabilistic logic programming in discrete-continuous domains

Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra, Angelika Kimmig

Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting

A. Das, W. Kong, B. Paria, R. Sen

CrysMMNet: Multimodal Representation for Crystal Property Prediction

Kishalay Das, Pawan Goyal, Seung-Cheol Lee, Satadeep Bhattacharjee, Niloy Ganguly

Improvable Gap Balancing for Multi-Task Learning

Yanqi Dai, Nanyi Fei, Zhiwu Lu

Conditional abstraction trees for sample-efficient reinforcement learning

Mehdi Dadvar, Rashmeet Kaur Nayyar, Siddharth Srivastava

Blackbox optimization of unimodal functions

A. Cutkosky, A. Das, W. Kong, C. Lee, R. Sen

Learning to reason about contextual knowledge for planning under uncertainty

Cheng Cui, Saeid Amiri, Yan Ding, Xingyue Zhan, Shiqi Zhang