UAI

Publications

2023

Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang
Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter. UAI (2023)
Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth
Inference for mark-censored temporal point processes. UAI (2023)
Lisa Wimmer, Yusuf Sale, Paul Hofman, Bernd Bischl, Eyke Hüllermeier
Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? UAI (2023)
Tom Hochsprung, Jonas Wahl, Andreas Gerhardus, Urmi Ninad, Jakob Runge
Increasing effect sizes of pairwise conditional independence tests between random vectors. UAI (2023)
Jinglong Luo, Yehong Zhang, Jiaqi Zhang, Shuang Qin, Hui Wang, Yue Yu, Zenglin Xu
Practical privacy-preserving Gaussian process regression via secret sharing. UAI (2023)
Jeonggeun Song, Heung-Chang Lee
ViBid: Linear Vision Transformer with Bidirectional Normalization. UAI (2023)
Ruiqi Zhao, Lei Zhang, Shengyu Zhu, Zitong Lu, Zhenhua Dong, Chaoliang Zhang, Jun Xu, Zhi Geng, Yangbo He
Conditional counterfactual causal effect for individual attribution. UAI (2023)
Felix L. Opolka, Yin-Cong Zhi, Pietro Liò, Xiaowen Dong
Graph classification Gaussian processes via spectral features. UAI (2023)
Sahil Garg, Mina Dalirrooyfard, Anderson Schneider, Yeshaya Adler, Yuriy Nevmyvaka, Yu Chen, Fengpei Li, Guillermo A. Cecchi
Information theoretic clustering via divergence maximization among clusters. UAI (2023)
Swetha Ganesh, Rohan Deb, Gugan Thoppe, Amarjit Budhiraja
Does Momentum Help in Stochastic Optimization? A Sample Complexity Analysis. UAI (2023)
Tom Claassen, Joris M. Mooij
Establishing Markov equivalence in cyclic directed graphs. UAI (2023)
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves
BISCUIT: Causal Representation Learning from Binary Interactions. UAI (2023)
Oliver E. Richardson, Joseph Y. Halpern, Christopher De Sa
Inference for probabilistic dependency graphs. UAI (2023)
Viplove Arora, Daniele Irto, Sebastian Goldt, Guido Sanguinetti
Quantifying lottery tickets under label noise: accuracy, calibration, and complexity. UAI (2023)
Abishek Sankararaman, Balakrishnan Narayanaswamy
Online Heavy-tailed Change-point detection. UAI (2023)
Andrzej Nagórko, Pawel Ciosmak, Tomasz P. Michalak
Two-phase Attacks in Security Games. UAI (2023)
Felix Schur, Parnian Kassraie, Jonas Rothfuss, Andreas Krause
Lifelong bandit optimization: no prior and no regret. UAI (2023)
Jiahao Li, Yiqiang Chen, Yunbing Xing
Memory Mechanism for Unsupervised Anomaly Detection. UAI (2023)
Vikrant Rangnekar, Uddeshya Upadhyay, Zeynep Akata, Biplab Banerjee
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution. UAI (2023)
Jinyan Su, Changhong Zhao, Di Wang
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited. UAI (2023)
Belhal Karimi, Ping Li, Xiaoyun Li
Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning. UAI (2023)
Renjun Xu, Kaifan Yang, Ke Liu, Fengxiang He
E(2)-Equivariant Vision Transformer. UAI (2023)
Minhyun Kang, Gi-Soo Kim
Heavy-tailed linear bandit with Huber regression. UAI (2023)
Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang, Donggeon Lee, Sang Woo Kim
How to use dropout correctly on residual networks with batch normalization. UAI (2023)
Arpan Losalka, Jonathan Scarlett
Benefits of monotonicity in safe exploration with Gaussian processes. UAI (2023)
Tengfei Ma, Trong Nghia Hoang, Jie Chen
Federated learning of models pre-trained on different features with consensus graphs. UAI (2023)
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy
Approximate Thompson Sampling via Epistemic Neural Networks. UAI (2023)
Ali Shahin Shamsabadi, Jamie Hayes, Borja Balle, Adrian Weller
Mnemonist: Locating Model Parameters that Memorize Training Examples. UAI (2023)
Yue Wu, Jiafan He, Quanquan Gu
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension. UAI (2023)
Sami Abu-El-Haija, Joshua V. Dillon, Bahare Fatemi, Kyriakos Axiotis, Neslihan Bulut, Johannes Gasteiger, Bryan Perozzi, MohammadHossein Bateni
SubMix: Learning to Mix Graph Sampling Heuristics. UAI (2023)
Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin
Approximately Bayes-optimal pseudo-label selection. UAI (2023)
Duanxiao Song, Guangyuan Shen, Dehong Gao, Libin Yang, Xukai Zhou, Shirui Pan, Wei Lou, Fang Zhou
Fast Heterogeneous Federated Learning with Hybrid Client Selection. UAI (2023)
Jiazheng Li, Zhaoyue Sun, Bin Liang, Lin Gui, Yulan He
CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models. UAI (2023)
Ali Hasan, Yu Chen, Yuting Ng, Mohamed Abdelghani, Anderson Schneider, Vahid Tarokh
Inference and sampling of point processes from diffusion excursions. UAI (2023)
Siqi Chen, Jianing Zhao, Gerhard Weiss, Ran Su, Kaiyou Lei
An effective negotiating agent framework based on deep offline reinforcement learning. UAI (2023)
Yassir Fathullah, Guoxuan Xia, Mark J. F. Gales
Logit-based ensemble distribution distillation for robust autoregressive sequence uncertainties. UAI (2023)
Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick
Exploiting Inferential Structure in Neural Processes. UAI (2023)
Madhavan R. Padmanabhan, Yanhui Zhu, Samik Basu, Aduri Pavan
Maximizing submodular functions under submodular constraints. UAI (2023)
Anna Guo, Jiwei Zhao, Razieh Nabi
Sufficient identification conditions and semiparametric estimation under missing not at random mechanisms. UAI (2023)
Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa
Functional causal Bayesian optimization. UAI (2023)
Wenqian Ye, Yunsheng Ma, Xu Cao, Kun Tang
Mitigating Transformer Overconfidence via Lipschitz Regularization. UAI (2023)
Youngseog Chung, Aaron Rumack, Chirag Gupta
Parity calibration. UAI (2023)
Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Guillaud
Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective. UAI (2023)
Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik
Random Reshuffling with Variance Reduction: New Analysis and Better Rates. UAI (2023)
Dustin Morrill, Thomas J. Walsh, Daniel Hernandez, Peter R. Wurman, Peter Stone
Composing Efficient, Robust Tests for Policy Selection. UAI (2023)
Fangchen Yu, Yicheng Zeng, Jianfeng Mao, Wenye Li
Online estimation of similarity matrices with incomplete data. UAI (2023)
Ziqian Zhang, Lei Yuan, Lihe Li, Ke Xue, Chengxing Jia, Cong Guan, Chao Qian, Yang Yu
Fast Teammate Adaptation in the Presence of Sudden Policy Change. UAI (2023)
Chengmin Gao, Bin Li
Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and Prediction. UAI (2023)
Cuong N. Nguyen, Phong Tran, Lam Si Tung Ho, Vu C. Dinh, Anh T. Tran, Tal Hassner, Cuong V. Nguyen
Simple Transferability Estimation for Regression Tasks. UAI (2023)

volume 216, 2023

Uncertainty in Artificial Intelligence, UAI 2023, July 31 - 4 August 2023, Pittsburgh, PA, USA. UAI 216 (2023)