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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 - Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg:
Partial identification of dose responses with hidden confounders. 1368-1379 - Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan:
Knowledge Intensive Learning of Cutset Networks. 1380-1389 - Takuo Matsubara, Niek Tax, Richard Mudd, Ido Guy:
TCE: A Test-Based Approach to Measuring Calibration Error. 1390-1400 - Bijan Mazaheri, Atalanti-Anastasia Mastakouri, Dominik Janzing, Michaela Hardt:
Causal information splitting: Engineering proxy features for robustness to distribution shifts. 1401-1411 - Jonathan Mei, Alexander Moreno, Luke Walters:
KrADagrad: Kronecker approximation-domination gradient preconditioned stochastic optimization. 1412-1422 - Alberto Maria Metelli, Samuele Meta, Marcello Restelli:
On the Relation between Policy Improvement and Off-Policy Minimum-Variance Policy Evaluation. 1423-1433 - Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju:
On Minimizing the Impact of Dataset Shifts on Actionable Explanations. 1434-1444 - Peiman Mohseni, Nick Duffield, Bani K. Mallick, Arman Hasanzadeh:
Adaptive Conditional Quantile Neural Processes. 1445-1455 - Dustin Morrill, Thomas J. Walsh, Daniel Hernandez, Peter R. Wurman, Peter Stone:
Composing Efficient, Robust Tests for Policy Selection. 1456-1466 - Razieh Nabi, Rohit Bhattacharya:
On Testability and Goodness of Fit Tests in Missing Data Models. 1467-1477 - Namrata Nadagouda, Austin Xu, Mark A. Davenport:
Active metric learning and classification using similarity queries. 1478-1488 - Andrzej Nagórko, Pawel Ciosmak, Tomasz P. Michalak:
Two-phase Attacks in Security Games. 1489-1498 - Sushirdeep Narayana, Ian A. Kash:
Keep-Alive Caching for the Hawkes process. 1499-1509 - 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. 1510-1521 - Vu-Linh Nguyen, Yang Yang, Cassio de Campos:
Probabilistic Multi-Dimensional Classification. 1522-1533 - Bao Nguyen, Viet Anh Nguyen:
Efficient Failure Pattern Identification of Predictive Algorithms. 1534-1544 - Guanyu Nie, Yanhui Zhu, Yididiya Y. Nadew, Samik Basu, A. Pavan, Christopher John Quinn:
Size-constrained k-submodular maximization in near-linear time. 1545-1554 - Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison:
An improved variational approximate posterior for the deep Wishart process. 1555-1563 - Caspar Oesterheld, Johannes Treutlein, Emery Cooper, Rubi Hudson:
Incentivizing honest performative predictions with proper scoring rules. 1564-1574 - Felix L. Opolka, Yin-Cong Zhi, Pietro Liò, Xiaowen Dong:
Graph classification Gaussian processes via spectral features. 1575-1585 - Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy:
Approximate Thompson Sampling via Epistemic Neural Networks. 1586-1595 - Pierre Osselin, Henry Kenlay, Xiaowen Dong:
Structure-aware robustness certificates for graph classification. 1596-1605 - Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol:
Baysian numerical integration with neural networks. 1606-1617 - Madhavan R. Padmanabhan, Yanhui Zhu, Samik Basu, Aduri Pavan:
Maximizing submodular functions under submodular constraints. 1618-1627 - Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio:
Stochastic Generative Flow Networks. 1628-1638 - Teodora Pandeva, Patrick Forré:
Multi-View Independent Component Analysis with Shared and Individual Sources. 1639-1650 - Hanyu Peng, Guanhua Fang, Ping Li:
Copula for Instance-wise Feature Selection and Rank. 1651-1661 - Bobak Pezeshki, Radu Marinescu, Alexander Ihler, Rina Dechter:
Boosting AND/OR-based computational protein design: dynamic heuristics and generalizable UFO. 1662-1672 - Pawel Piwek, Adam Klukowski, Tianyang Hu:
Exact Count of Boundary Pieces of ReLU Classifiers: Towards the Proper Complexity Measure for Classification. 1673-1683 - Michael Purcell, Yang Li, Kee Siong Ng:
Split, count, and share: a differentially private set intersection cardinality estimation protocol. 1684-1694 - Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner:
Jana: Jointly amortized neural approximation of complex Bayesian models. 1695-1706 - Vikrant Rangnekar, Uddeshya Upadhyay, Zeynep Akata, Biplab Banerjee:
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution. 1707-1717 - Gathika Ratnayaka, Qing Wang, Yang Li:
Contrastive learning for supervised graph matching. 1718-1729 - David Reeb, Kanil Patel, Karim Said Barsim, Martin Schiegg, Sebastian Gerwinn:
Validation of composite systems by discrepancy propagation. 1730-1740 - Oliver E. Richardson, Joseph Y. Halpern, Christopher De Sa:
Inference for probabilistic dependency graphs. 1741-1751 - Steffen Ridderbusch, Sina Ober-Blöbaum, Paul Goulart:
The past does matter: correlation of subsequent states in trajectory predictions of Gaussian Process models. 1752-1761 - Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin:
Approximately Bayes-optimal pseudo-label selection. 1762-1773 - Jonas Rothfuss, Bhavya Sukhija, Tobias Birchler, Parnian Kassraie, Andreas Krause:
Hallucinated adversarial control for conservative offline policy evaluation. 1774-1784 - Michael Rotman, Amit Dekel, Ran Ilan Ber, Lior Wolf, Yaron Oz:
Semi-supervised learning of partial differential operators and dynamical flows. 1785-1794 - Yusuf Sale, Michele Caprio, Eyke Hüllermeier:
Is the volume of a credal set a good measure for epistemic uncertainty? 1795-1804 - Colin Samplawski, Shiwei Fang, Ziqi Wang, Deepak Ganesan, Mani B. Srivastava, Benjamin M. Marlin:
Heteroskedastic Geospatial Tracking with Distributed Camera Networks. 1805-1814 - Abishek Sankararaman, Balakrishnan Narayanaswamy:
Online Heavy-tailed Change-point detection. 1815-1826 - Ayush Sawarni, Rahul Madhavan, Gaurav Sinha, Siddharth Barman:
Learning good interventions in causal graphs via covering. 1827-1836 - Luca Schmid, Joshua Brenk, Laurent Schmalen:
Local Message Passing on Frustrated Systems. 1837-1846 - Felix Schur, Parnian Kassraie, Jonas Rothfuss, Andreas Krause:
Lifelong bandit optimization: no prior and no regret. 1847-1857 - Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Guillaud:
Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective. 1858-1867 - Seunghyeon Seo, Jaeyoung Yoo, Jihye Hwang, Nojun Kwak:
MDPose: real-time multi-person pose estimation via mixture density model. 1868-1878 - Ali Shahin Shamsabadi, Jamie Hayes, Borja Balle, Adrian Weller:
Mnemonist: Locating Model Parameters that Memorize Training Examples. 1879-1888 - Shiv Shankar:
Implicit Training of Inference Network Models for Structured Prediction. 1889-1899 - Dravyansh Sharma, Maxwell Jones:
Efficiently learning the graph for semi-supervised learning. 1900-1910 - Vidya Sagar Sharma:
Counting Background Knowledge Consistent Markov Equivalent Directed Acyclic Graphs. 1911-1920 - Vishal Sharma, Daman Arora, Mausam, Parag Singla:
SymNet 3.0: Exploiting Long-Range Influences in Learning Generalized Neural Policies for Relational MDPs. 1921-1931 - Shubhanshu Shekhar, Ziyu Xu, Zachary C. Lipton, Pierre J. Liang, Aaditya Ramdas:
Risk-limiting financial audits via weighted sampling without replacement. 1932-1941 - Wenqi Shi, Wenkai Xu:
Learning Nonlinear Causal Effect via Kernel Anchor Regression. 1942-1952 - Zai Shi, Jian Tan, Feifei Li:
A Bayesian approach for bandit online optimization with switching cost. 1953-1963 - Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan:
Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference. 1964-1973 - Joar Skalse, Alessandro Abate:
On the limitations of Markovian rewards to express multi-objective, risk-sensitive, and modal tasks. 1974-1984 - Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, María Rodríguez Martínez, Andreas Krause, Charlotte Bunne:
Aligned Diffusion Schrödinger Bridges. 1985-1995 - Jeonggeun Song, Heung-Chang Lee:
ViBid: Linear Vision Transformer with Bidirectional Normalization. 1996-2005 - Duanxiao Song, Guangyuan Shen, Dehong Gao, Libin Yang, Xukai Zhou, Shirui Pan, Wei Lou, Fang Zhou:
Fast Heterogeneous Federated Learning with Hybrid Client Selection. 2006-2015 - Xihong Su, Marek Petrik:
Solving multi-model MDPs by coordinate ascent and dynamic programming. 2016-2025 - Jinyan Su, Changhong Zhao, Di Wang:
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited. 2026-2035 - Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins, Raja Marjieh, Joshua C. Peterson, Pulkit Singh, Umang Bhatt, Nori Jacoby, Adrian Weller, Thomas L. Griffiths:
On the informativeness of supervision signals. 2036-2046 - Zhuo Sun, Chris J. Oates, François-Xavier Briol:
Meta-learning Control Variates: Variance Reduction with Limited Data. 2047-2057 - Xiaolin Sun, Jacob Masur, Ben Abramowitz, Nicholas Mattei, Zizhan Zheng:
Pandering in a (flexible) representative democracy. 2058-2068 - Dongfang Sun, Yingzhen Yang:
Locally Regularized Sparse Graph by Fast Proximal Gradient Descent. 2069-2077 - Kamil Szyc, Tomasz Walkowiak, Henryk Maciejewski:
Why Out-of-Distribution detection experiments are not reliable - subtle experimental details muddle the OOD detector rankings. 2078-2088 - Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick:
Exploiting Inferential Structure in Neural Processes. 2089-2098 - Jian Tan, Niv Nayman:
Two-stage Kernel Bayesian Optimization in High Dimensions. 2099-2110 - Zhiwei Tang, Tsung-Hui Chang, Xiaojing Ye, Hongyuan Zha:
Low-rank matrix recovery with unknown correspondence. 2111-2122 - Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Qi Long, Li Shen:
Fairness-aware class imbalanced learning on multiple subgroups. 2123-2133 - Vithursan Thangarasa, Abhay Gupta, William Marshall, Tianda Li, Kevin Leong, Dennis DeCoste, Sean Lie, Shreyas Saxena:
SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models. 2134-2146 - Aaron David Tucker, Caleb Biddulph, Claire Wang, Thorsten Joachims:
Bandits with costly reward observations. 2147-2156 - Fabrizio Ventola, Steven Braun, Zhongjie Yu, Martin Mundt, Kristian Kersting:
Probabilistic circuits that know what they don't know. 2157-2167 - Nithia Vijayan, Prashanth L. A.:
A policy gradient approach for optimization of smooth risk measures. 2168-2178 - Yoav Wald, Suchi Saria:
Birds of an odd feather: guaranteed out-of-distribution (OOD) novel category detection. 2179-2191 - Xuchuang Wang, Lin Yang, Yu-Zhen Janice Chen, Xutong Liu, Mohammad Hajiesmaili, Don Towsley, John C. S. Lui:
Exploration for Free: How Does Reward Heterogeneity Improve Regret in Cooperative Multi-agent Bandits? 2192-2202 - Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu:
Efficient Privacy-Preserving Stochastic Nonconvex Optimization. 2203-2213 - Hanjing Wang, Qiang Ji:
Diversity-enhanced probabilistic ensemble for uncertainty estimation. 2214-2225 - Yiqi Wang, Mengdi Xu, Laixi Shi, Yuejie Chi:
A trajectory is worth three sentences: multimodal transformer for offline reinforcement learning. 2226-2236 - Serena Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, Aditya Krishna Menon:
Robust distillation for worst-class performance: on the interplay between teacher and student objectives. 2237-2247 - Ziyu Wang, Binjie Yuan, Jiaxun Lu, Bowen Ding, Yunfeng Shao, Qibin Wu, Jun Zhu:
A constrained Bayesian approach to out-of-distribution prediction. 2248-2258 - Yilin Wang, Farzan Farnia:
On the Role of Generalization in Transferability of Adversarial Examples. 2259-2270 - Kevin Christian Wibisono, Yixin Wang:
Bidirectional Attention as a Mixture of Continuous Word Experts. 2271-2281 - 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? 2282-2292 - Ruihan Wu, Xiangyu Chen, Chuan Guo, Kilian Q. Weinberger:
Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning. 2293-2303 - Yue Wu, Jiafan He, Quanquan Gu:
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension. 2304-2313 - Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh:
Robust Quickest Change Detection for Unnormalized Models. 2314-2323 - Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi:
A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization. 2324-2334 - Weiyan Xie, Xiao-Hui Li, Zhi Lin, Leonard K. M. Poon, Caleb Chen Cao, Nevin L. Zhang:
Two-stage holistic and contrastive explanation of image classification. 2335-2345 - Yunpeng Xu, Wenge Guo, Zhi Wei:
Conformal Risk Control for Ordinal Classification. 2346-2355 - Renjun Xu, Kaifan Yang, Ke Liu, Fengxiang He:
E(2)-Equivariant Vision Transformer. 2356-2366 - Tian Xu, Ziniu Li, Yang Yu, Zhi-Quan Luo:
Provably Efficient Adversarial Imitation Learning with Unknown Transitions. 2367-2378 - Chao-Han Huck Yang, Zhengling Qi, Yifan Cui, Pin-Yu Chen:
Pessimistic Model Selection for Offline Deep Reinforcement Learning. 2379-2389 - Yongxin Yang, Timothy M. Hospedales:
Mixture of Normalizing Flows for European Option Pricing. 2390-2399 - Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe:
Multi-modal differentiable unsupervised feature selection. 2400-2410 - Chengmei Yang, Bowei He, Yimeng Wu, Chao Xing, Lianghua He, Chen Ma:
MMEL: A Joint Learning Framework for Multi-Mention Entity Linking. 2411-2421 - Wenqian Ye, Yunsheng Ma, Xu Cao, Kun Tang:
Mitigating Transformer Overconfidence via Lipschitz Regularization. 2422-2432 - Seunghoon Yi, Youngwoo Cho, Jinhwan Sul, Seung Woo Ko, Soo Kyung Kim, Jaegul Choo, Hongkee Yoon, Joonseok Lee:
Towards Physically Reliable Molecular Representation Learning. 2433-2443 - Yang You, Vincent Thomas, Francis Colas, Olivier Buffet:
Monte-Carlo Search for an Equilibrium in Dec-POMDPs. 2444-2453 - Fangchen Yu, Yicheng Zeng, Jianfeng Mao, Wenye Li:
Online estimation of similarity matrices with incomplete data. 2454-2464 - 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. 2465-2476 - Tianjun Zhang, Tongzheng Ren, Chenjun Xiao, Wenli Xiao, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai:
Energy-based Predictive Representations for Partially Observed Reinforcement Learning. 2477-2487 - Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu:
Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL. 2488-2497 - Tianyi Zhang, Zhenwei Dai, Zhaozhuo Xu, Anshumali Shrivastava:
Graph Self-supervised Learning via Proximity Distribution Minimization. 2498-2508 - Hang Zhang, Ping Li:
Greed is good: correspondence recovery for unlabeled linear regression. 2509-2518 - 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. 2519-2528 - Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar, Janarthanan Rajendran:
Conditionally optimistic exploration for cooperative deep multi-agent reinforcement learning. 2529-2540 - Tianhang Zheng, Baochun Li:
RDM-DC: Poisoning Resilient Dataset Condensation with Robust Distribution Matching. 2541-2550 - Qi Zhou, Jie Wang, Qiyuan Liu, Yufei Kuang, Wengang Zhou, Houqiang Li:
Learning robust representation for reinforcement learning with distractions by reward sequence prediction. 2551-2562 - Zhaoyi Zhou, Zaiwei Chen, Yiheng Lin, Adam Wierman:
Convergence rates for localized actor-critic in networked Markov potential games. 2563-2573 - Xiangyu Zhu, Jie Hao, Yunhui Guo, Mingrui Liu:
AUC Maximization in Imbalanced Lifelong Learning. 2574-2585 - Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang:
Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter. 2586-2596 - Yingtian Zou, Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham, Juho Kannala, Yoshua Bengio, Arno Solin, Kenji Kawaguchi:
MixupE: Understanding and improving Mixup from directional derivative perspective. 2597-2607 - Pengyu Zuo, Yao Wang, Shaojie Tang:
Regularized online DR-submodular optimization. 2608-2617
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