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ORCID39th UAI 2023: Pittsburgh, PA, USA
- Robin J. Evans, Ilya Shpitser:
Uncertainty in Artificial Intelligence, UAI 2023, July 31 - 4 August 2023, Pittsburgh, PA, USA. Proceedings of Machine Learning Research 216, PMLR 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. 1-10 - Idan Achituve, Gal Chechik, Ethan Fetaya:
Guided Deep Kernel Learning. 11-21 - Jacob Adamczyk, Volodymyr Makarenko, Argenis Arriojas, Stas Tiomkin, Rahul V. Kulkarni:
Bounding the optimal value function in compositional reinforcement learning. 22-32 - Sakshi Agarwal, Gabriel Hope, Ali Younis, Erik B. Sudderth:
A decoder suffices for query-adaptive variational inference. 33-44 - Md. Ibrahim Ibne Alam, Koushik Kar, Theodoros Salonidis, Horst Samulowitz:
FLASH: Automating federated learning using CASH. 45-55 - Ahmed Aloui, Juncheng Dong, Cat P. Le, Vahid Tarokh:
Transfer learning for individual treatment effect estimation. 56-66 - Daniel Andrade, Akiko Takeda:
Robust Gaussian process regression with the trimmed marginal likelihood. 67-76 - Shuang Ao, Stefan Rueger, Advaith Siddharthan:
Two Sides of Miscalibration: Identifying Over and Under-Confidence Prediction for Network Calibration. 77-87 - Viplove Arora, Daniele Irto, Sebastian Goldt, Guido Sanguinetti:
Quantifying lottery tickets under label noise: accuracy, calibration, and complexity. 88-98 - Argenis Arriojas, Jacob Adamczyk, Stas Tiomkin, Rahul V. Kulkarni:
Bayesian inference approach for entropy regularized reinforcement learning with stochastic dynamics. 99-109 - Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu, Mikhail Belkin:
Neural tangent kernel at initialization: linear width suffices. 110-118 - Christine W. Bang, Vanessa Didelez:
Do we become wiser with time? On causal equivalence with tiered background knowledge. 119-129 - Baptiste Bauvin, Cécile Capponi, Florence Clerc, Pascal Germain, Sokol Koço, Jacques Corbeil:
Sample Boosting Algorithm (SamBA) - An interpretable greedy ensemble classifier based on local expertise for fat data. 130-140 - Alessio Benavoli, Dario Azzimonti, Dario Piga:
Learning Choice Functions with Gaussian Processes. 141-151 - Petra Berenbrink, Max Hahn-Klimroth
, Dominik Kaaser, Lena Krieg, Malin Rau:
Inference of a rumor's source in the independent cascade model. 152-162 - Anirban Bhattacharjee, Sushant Vijayan, Sandeep Juneja:
Best arm identification in rare events. 163-172 - Valentin Bieri, Paul Streli, Berken Utku Demirel, Christian Holz:
BeliefPPG: Uncertainty-aware heart rate estimation from PPG signals via belief propagation. 173-183 - Matthias Bitzer, Mona Meister, Christoph Zimmer:
Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels. 184-194 - Philip A. Boeken, Noud de Kroon, Mathijs de Jong, Joris M. Mooij, Onno Zoeter:
Correcting for selection bias and missing response in regression using privileged information. 195-205 - Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez:
Efficient Learning of Minimax Risk Classifiers in High Dimensions. 206-215 - Louenas Bounia, Frédéric Koriche:
Approximating probabilistic explanations via supermodular minimization. 216-225 - Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth:
Inference for mark-censored temporal point processes. 226-236 - Noah Burrell, Grant Schoenebeck:
Testing conventional wisdom (of the crowd). 237-248 - Runlin Cao, Zhixin Li:
Overcoming Language Priors for Visual Question Answering via Loss Rebalancing Label and Global Context. 249-259 - William X. Cao, Poorva Garg, Ryan Tjoa, Steven Holtzen, Todd D. Millstein, Guy Van den Broeck:
Scaling integer arithmetic in probabilistic programs. 260-270 - Ryan Carey, Tom Everitt:
Human Control: Definitions and Algorithms. 271-281 - Sunrit Chakraborty, Aritra Guha, Rayleigh Lei, XuanLong Nguyen:
Scalable nonparametric Bayesian learning for dynamic velocity fields. 282-292 - Gautam Chandrasekaran, Ambuj Tewari:
Learning in online MDPs: is there a price for handling the communicating case? 293-302 - Xingguo Chen, Xingzhou Ma, Yang Li, Guang Yang, Shangdong Yang, Yang Gao:
Modified Retrace for Off-Policy Temporal Difference Learning. 303-312 - Jinghui Chen, Yuan Cao, Quanquan Gu:
Benign Overfitting in Adversarially Robust Linear Classification. 313-323 - Siqi Chen, Jianing Zhao, Gerhard Weiss, Ran Su, Kaiyou Lei:
An effective negotiating agent framework based on deep offline reinforcement learning. 324-335 - Wen Chen, Yushan Zhang, Zhiheng Li, Yuehuan Wang:
MFA: Multi-layer Feature-aware Attack for Object Detection. 336-346 - Bo Chen, Calvin Hawkins, Mustafa O. Karabag, Cyrus Neary, Matthew T. Hale, Ufuk Topcu:
Differential Privacy in Cooperative Multiagent Planning. 347-357 - Jacob M. Chen, Daniel Malinsky, Rohit Bhattacharya:
Causal inference with outcome-dependent missingness and self-censoring. 358-368 - Yu Chen, Fengpei Li, Anderson Schneider, Yuriy Nevmyvaka, Asohan Amarasingham, Henry Lam:
Detection of Short-Term Temporal Dependencies in Hawkes Processes with Heterogeneous Background Dynamics. 369-380 - Yuwen Cheng, Lili Wu, Shu Yang:
Enhancing Treatment Effect Estimation: A Model Robust Approach Integrating Randomized Experiments and External Controls using the Double Penalty Integration Estimator. 381-390 - Davin Choo, Kirankumar Shiragur:
Adaptivity Complexity for Causal Graph Discovery. 391-402 - Sayak Ray Chowdhury, Gaurav Sinha, Nagarajan Natarajan, Amit Sharma:
Combinatorial categorized bandits with expert rankings. 403-412 - Youngseog Chung, Aaron Rumack, Chirag Gupta:
Parity calibration. 413-423 - Pedro Cisneros-Velarde, Sanmi Koyejo:
Finite-sample guarantees for Nash Q-learning with linear function approximation. 424-432 - Tom Claassen, Joris M. Mooij:
Establishing Markov equivalence in cyclic directed graphs. 433-442 - Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová:
Expectation consistency for calibration of neural networks. 443-453 - Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley C. Love, Adrian Weller:
Human-in-the-Loop Mixup. 454-464 - Cheng Cui, Saeid Amiri, Yan Ding, Xingyue Zhan, Shiqi Zhang:
Learning to reason about contextual knowledge for planning under uncertainty. 465-475 - Ashok Cutkosky, Abhimanyu Das, Weihao Kong, Chansoo Lee, Rajat Sen:
Blackbox optimization of unimodal functions. 476-484 - Mehdi Dadvar, Rashmeet Kaur Nayyar, Siddharth Srivastava:
Conditional abstraction trees for sample-efficient reinforcement learning. 485-495 - Yanqi Dai, Nanyi Fei, Zhiwu Lu:
Improvable Gap Balancing for Multi-Task Learning. 496-506 - Kishalay Das, Pawan Goyal, Seung-Cheol Lee, Satadeep Bhattacharjee, Niloy Ganguly:
CrysMMNet: Multimodal Representation for Crystal Property Prediction. 507-517 - Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen:
Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting. 518-528 - Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra, Angelika Kimmig, Luc De Raedt:
Neural probabilistic logic programming in discrete-continuous domains. 529-538 - Claire Donnat, Sowon Jeong:
Studying the Effect of GNN Spatial Convolutions On The Embedding Space's Geometry. 539-548 - Yousef El-Laham, Niccolò Dalmasso, Elizabeth Fons, Svitlana Vyetrenko:
Deep Gaussian mixture ensembles. 549-559 - Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang:
Personalized federated domain adaptation for item-to-item recommendation. 560-570 - Jiaojiao Fan, David Alvarez-Melis:
Generating Synthetic Datasets by Interpolating along Generalized Geodesics. 571-581 - Yassir Fathullah, Guoxuan Xia, Mark J. F. Gales:
Logit-based ensemble distribution distillation for robust autoregressive sequence uncertainties. 582-591 - Jonathan Foldager, Mikkel Jordahn, Lars Kai Hansen, Michael Riis Andersen:
On the role of model uncertainties in Bayesian optimisation. 592-601 - Swetha Ganesh, Rohan Deb, Gugan Thoppe, Amarjit Budhiraja:
Does Momentum Help in Stochastic Optimization? A Sample Complexity Analysis. 602-612 - Chengmin Gao, Bin Li:
Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and Prediction. 613-623 - 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. 624-634 - Sahil Garg, Sanghamitra Dutta, Mina Dalirrooyfard, Anderson Schneider, Yuriy Nevmyvaka:
In- or out-of-distribution detection via dual divergence estimation. 635-646 - Sinong Geng, Houssam Nassif, Carlos A. Manzanares:
A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models. 647-657 - Sahra Ghalebikesabi, Chris C. Holmes, Edwin Fong, Brieuc Lehmann:
Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates. 658-668 - Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner, Rahul G. Krishnan:
Copula-based deep survival models for dependent censoring. 669-680 - Subhankar Ghosh, Yuanjie Shi, Taha Belkhouja, Yan Yan, Jana Doppa, Brian Jones:
Probabilistically robust conformal prediction. 681-690 - Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton:
Fast and scalable score-based kernel calibration tests. 691-700 - Misha Glazunov, Apostolis Zarras:
Vacant holes for unsupervised detection of the outliers in compact latent representation. 701-711 - Ethan Goan, Dimitri Perrin, Kerrie L. Mengersen, Clinton Fookes:
Piecewise Deterministic Markov Processes for Bayesian Neural Networks. 712-722 - Ali Gorji, Andisheh Amrollahi, Andreas Krause:
A scalable Walsh-Hadamard regularizer to overcome the low-degree spectral bias of neural networks. 723-733 - Yutian Gou, Jinfeng Yi, Lijun Zhang:
Stochastic Graphical Bandits with Heavy-Tailed Rewards. 734-744 - Denis A. Gudovskiy, Tomoyuki Okuno, Yohei Nakata:
Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow. 745-755 - Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa:
Functional causal Bayesian optimization. 756-765 - Wiebke Günther, Urmi Ninad, Jakob Runge:
Causal Discovery for time series from multiple datasets with latent contexts. 766-776 - Anna Guo, Jiwei Zhao, Razieh Nabi:
Sufficient identification conditions and semiparametric estimation under missing not at random mechanisms. 777-787 - Swagatam Haldar, Diptikalyan Saha, Dennis Wei, Rahul Nair, Elizabeth M. Daly:
Interpretable differencing of machine learning models. 788-797 - Alex Hämäläinen, Mustafa Mert Çelikok, Samuel Kaski:
Differentiable user models. 798-808 - Seungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee:
On the Convergence of Continual Learning with Adaptive Methods. 809-818 - Juha Harviainen, Mikko Koivisto:
Revisiting Bayesian network learning with small vertex cover. 819-828 - Juha Harviainen, Vaidyanathan Peruvemba Ramaswamy, Mikko Koivisto:
On inference and learning with probabilistic generating circuits. 829-838 - Ali Hasan, Yu Chen, Yuting Ng, Mohamed Abdelghani, Anderson Schneider, Vahid Tarokh:
Inference and sampling of point processes from diffusion excursions. 839-848 - Jiamin He, Fengdi Che, Yi Wan, A. Rupam Mahmood:
Loosely consistent emphatic temporal-difference learning. 849-859 - Yicong He, George K. Atia:
Scalable and robust tensor ring decomposition for large-scale data. 860-869 - Thomas Heap, Gavin Leech, Laurence Aitchison:
Massively parallel reweighted wake-sleep. 870-878 - Tom Hochsprung, Jonas Wahl, Andreas Gerhardus, Urmi Ninad, Jakob Runge:
Increasing effect sizes of pairwise conditional independence tests between random vectors. 879-889 - Bingshan Hu, Tianyue H. Zhang, Nidhi Hegde, Mark Schmidt:
Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning. 890-899 - Zixin Huang, Saikat Dutta, Sasa Misailovic:
ASTRA: Understanding the practical impact of robustness for probabilistic programs. 900-910 - Zhiming Huang, Jianping Pan:
A near-optimal high-probability swap-Regret upper bound for multi-agent bandits in unknown general-sum games. 911-921 - Mehdi Jafarnia-Jahromi, Liyu Chen, Rahul Jain, Haipeng Luo:
Posterior sampling-based online learning for the stochastic shortest path model. 922-931 - Michael Jahn, Matthias Scheutz:
Investigating a Generalization of Probabilistic Material Implication and Bayesian Conditionals. 932-940 - Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann, Thomas Augustin:
Robust statistical comparison of random variables with locally varying scale of measurement. 941-952 - Jonghu Jeong, Minyong Cho, Philipp Benz, Tae-Hoon Kim:
Noisy adversarial representation learning for effective and efficient image obfuscation. 953-962 - Fengjuan Jia, Mengxiao Zhang, Jiamou Liu, Bakh Khoussainov:
Incentivising Diffusion while Preserving Differential Privacy. 963-972 - Feiran Jia, Chenxi Qiu, Sarah Rajtmajer, Anna Cinzia Squicciarini:
Content Sharing Design for Social Welfare in Networked Disclosure Game. 973-983 - Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang:
Multi-view graph contrastive learning for solving vehicle routing problems. 984-994 - Chuxuan Jiang, Geoff K. Nicholls, Jeong-Eun (Kate) Lee:
Bayesian inference for vertex-series-parallel partial orders. 995-1004 - Florian Kalinke, Zoltán Szabó:
Nyström M-Hilbert-Schmidt independence criterion. 1005-1015 - David Kaltenpoth, Jilles Vreeken:
Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition. 1016-1026 - Minhyun Kang, Gi-Soo Kim:
Heavy-tailed linear bandit with Huber regression. 1027-1036 - Belhal Karimi, Ping Li, Xiaoyun Li:
Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning. 1037-1046 - Karine Karine, Predrag V. Klasnja, Susan A. Murphy, Benjamin M. Marlin:
Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions. 1047-1057 - Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang, Donggeon Lee, Sang Woo Kim:
How to use dropout correctly on residual networks with batch normalization. 1058-1067 - Yeachan Kim, Seongyeon Kim, Ihyeok Seo, Bonggun Shin:
Phase-shifted adversarial training. 1068-1077 - Yaroslav Kivva, Jalal Etesami, Negar Kiyavash:
On Identifiability of Conditional Causal Effects. 1078-1086 - Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach:
Causal effect estimation from observational and interventional data through matrix weighted linear estimators. 1087-1097 - Taewook Ko, Yoonhyuk Choi, Chong-Kwon Kim:
Universal Graph Contrastive Learning with a Novel Laplacian Perturbation. 1098-1108 - Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu:
Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting. 1109-1120 - Cevahir Köprülü, Ufuk Topcu:
Reward-machine-guided, self-paced reinforcement learning. 1121-1131 - Cevahir Köprülü, Thiago D. Simão, Nils Jansen, Ufuk Topcu:
Risk-aware curriculum generation for heavy-tailed task distributions. 1132-1142 - Eleonora Kreacic, Navid Nouri, Vamsi K. Potluru, Tucker Balch, Manuela Veloso:
Differentially private synthetic data using KD-trees. 1143-1153 - Adithya Kulkarni, Mohna Chakraborty, Sihong Xie, Qi Li:
Optimal Budget Allocation for Crowdsourcing Labels for Graphs. 1154-1163 - Anusha Lalitha, Kousha Kalantari, Yifei Ma, Anoop Deoras, Branislav Kveton:
Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances. 1164-1173 - Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page:
Variable importance matching for causal inference. 1174-1184 - Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon, Marie-Pierre Revel, Siddharth Garg, Farshad Khorrami, Maria Vakalopoulou:
Towards better certified segmentation via diffusion models. 1185-1195 - Kenneth Lee, Md. Musfiqur Rahman, Murat Kocaoglu:
Finding Invariant Predictors Efficiently via Causal Structure. 1196-1206 - Tobias Leemann, Michael Kirchhof, Yao Rong, Enkelejda Kasneci, Gjergji Kasneci:
When are post-hoc conceptual explanations identifiable? 1207-1218 - Jiahao Li, Yiqiang Chen, Yunbing Xing:
Memory Mechanism for Unsupervised Anomaly Detection. 1219-1229 - Chris Junchi Li, Michael I. Jordan:
Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. 1230-1240 - Michael Y. Li, Erin Grant, Thomas L. Griffiths:
Gaussian Process Surrogate Models for Neural Networks. 1241-1252 - Jiazheng Li, Zhaoyue Sun, Bin Liang, Lin Gui, Yulan He:
CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models. 1253-1262 - Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves:
BISCUIT: Causal Representation Learning from Binary Interactions. 1263-1273 - Ao Liu, Qishen Han, Lirong Xia, Nengkun Yu:
Accelerating Voting by Quantum Computation. 1274-1283 - Shuheng Liu, Xiyue Huang, Pavlos Protopapas:
Residual-based error bound for physics-informed neural networks. 1284-1293 - Chong Liu, Ming Yin, Yu-Xiang Wang:
No-Regret Linear Bandits beyond Realizability. 1294-1303 - Arpan Losalka, Jonathan Scarlett:
Benefits of monotonicity in safe exploration with Gaussian processes. 1304-1314 - Jinglong Luo, Yehong Zhang, Jiaqi Zhang, Shuang Qin, Hui Wang, Yue Yu, Zenglin Xu:
Practical privacy-preserving Gaussian process regression via secret sharing. 1315-1325 - Ching-Wen Ma, Yanwei Liu:
DeepGD3: Unknown-Aware Deep Generative/Discriminative Hybrid Defect Detector for PCB Soldering Inspection. 1326-1335 - Tengfei Ma, Trong Nghia Hoang, Jie Chen:
Federated learning of models pre-trained on different features with consensus graphs. 1336-1346 - Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik:
Random Reshuffling with Variance Reduction: New Analysis and Better Rates. 1347-1357 - Charles C. Margossian, Lawrence K. Saul:
The Shrinkage-Delinkage Trade-off: an Analysis of Factorized Gaussian Approximations for Variational Inference. 1358-1367