Table of Contents

• SubMix: Learning to Mix Graph Sampling HeuristicsSami Abu-El-Haija, Joshua V. Dillon, Bahare Fatemi, Kyriakos Axiotis, Neslihan Bulut, Johannes Gasteiger, Bryan Perozzi, MohammadHossein Bateni. 1-10 [doi]
• Guided Deep Kernel LearningIdan Achituve, Gal Chechik, Ethan Fetaya. 11-21 [doi]
• Bounding the optimal value function in compositional reinforcement learningJacob Adamczyk, Volodymyr Makarenko, Argenis Arriojas, Stas Tiomkin, Rahul V. Kulkarni. 22-32 [doi]
• A decoder suffices for query-adaptive variational inferenceSakshi Agarwal, Gabriel Hope, Ali Younis, Erik B. Sudderth. 33-44 [doi]
• FLASH: Automating federated learning using CASHMd I. I. Alam, Koushik Kar, Theodoros Salonidis, Horst Samulowitz. 45-55 [doi]
• Transfer learning for individual treatment effect estimationAhmed Aloui, Juncheng Dong, Cat P. Le, Vahid Tarokh. 56-66 [doi]
• Robust Gaussian process regression with the trimmed marginal likelihoodDaniel Andrade, Akiko Takeda. 67-76 [doi]
• Two Sides of Miscalibration: Identifying Over and Under-Confidence Prediction for Network CalibrationShuang Ao, Stefan Rueger, Advaith Siddharthan. 77-87 [doi]
• Quantifying lottery tickets under label noise: accuracy, calibration, and complexityViplove Arora, Daniele Irto, Sebastian Goldt, Guido Sanguinetti. 88-98 [doi]
• Bayesian inference approach for entropy regularized reinforcement learning with stochastic dynamicsArgenis Arriojas, Jacob Adamczyk, Stas Tiomkin, Rahul V. Kulkarni. 99-109 [doi]
• Neural tangent kernel at initialization: linear width sufficesArindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu, Mikhail Belkin. 110-118 [doi]
• Do we become wiser with time? On causal equivalence with tiered background knowledgeChristine W. Bang, Vanessa Didelez. 119-129 [doi]
• Sample Boosting Algorithm (SamBA) - An interpretable greedy ensemble classifier based on local expertise for fat dataBaptiste Bauvin, Cécile Capponi, Florence Clerc, Pascal Germain, Sokol Koço, Jacques Corbeil. 130-140 [doi]
• Learning Choice Functions with Gaussian ProcessesAlessio Benavoli, Dario Azzimonti, Dario Piga. 141-151 [doi]
• Inference of a rumor's source in the independent cascade modelPetra Berenbrink, Max Hahn-Klimroth, Dominik Kaaser, Lena Krieg, Malin Rau. 152-162 [doi]
• Best arm identification in rare eventsAnirban Bhattacharjee, Sushant Vijayan, Sandeep Juneja 0001. 163-172 [doi]
• BeliefPPG: Uncertainty-aware heart rate estimation from PPG signals via belief propagationValentin Bieri, Paul Streli, Berken Utku Demirel, Christian Holz 0001. 173-183 [doi]
• Amortized Inference for Gaussian Process Hyperparameters of Structured KernelsMatthias Bitzer, Mona Meister, Christoph Zimmer. 184-194 [doi]
• Correcting for selection bias and missing response in regression using privileged informationPhilip A. Boeken, Noud de Kroon, Mathijs de Jong, Joris M. Mooij, Onno Zoeter. 195-205 [doi]
• Efficient Learning of Minimax Risk Classifiers in High DimensionsKartheek Bondugula, Santiago Mazuelas, Aritz Pérez. 206-215 [doi]
• Approximating probabilistic explanations via supermodular minimizationLouenas Bounia, Frédéric Koriche. 216-225 [doi]
• Inference for mark-censored temporal point processesAlex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth. 226-236 [doi]
• Testing conventional wisdom (of the crowd)Noah Burrell, Grant Schoenebeck. 237-248 [doi]
• Overcoming Language Priors for Visual Question Answering via Loss Rebalancing Label and Global ContextRunlin Cao, Zhixin Li 0001. 249-259 [doi]
• Scaling integer arithmetic in probabilistic programsWilliam X. Cao, Poorva Garg, Ryan Tjoa, Steven Holtzen, Todd D. Millstein, Guy Van den Broeck. 260-270 [doi]
• Human Control: Definitions and AlgorithmsRyan Carey, Tom Everitt. 271-281 [doi]
• Scalable nonparametric Bayesian learning for dynamic velocity fieldsSunrit Chakraborty, Aritra Guha, Rayleigh Lei, XuanLong Nguyen. 282-292 [doi]
• Learning in online MDPs: is there a price for handling the communicating case?Gautam Chandrasekaran, Ambuj Tewari. 293-302 [doi]
• Modified Retrace for Off-Policy Temporal Difference LearningXingguo Chen, Xingzhou Ma, Yang Li, Guang Yang, Shangdong Yang, Yang Gao. 303-312 [doi]
• Benign Overfitting in Adversarially Robust Linear ClassificationJinghui Chen, Yuan Cao 0006, Quanquan Gu. 313-323 [doi]
• An effective negotiating agent framework based on deep offline reinforcement learningSiqi Chen, Jianing Zhao, Gerhard Weiss 0001, Ran Su, Kaiyou Lei. 324-335 [doi]
• MFA: Multi-layer Feature-aware Attack for Object DetectionWen Chen, Yushan Zhang, Zhiheng Li, Yuehuan Wang. 336-346 [doi]
• Differential Privacy in Cooperative Multiagent PlanningBo Chen, Calvin Hawkins, Mustafa O. Karabag, Cyrus Neary, Matthew T. Hale, Ufuk Topcu. 347-357 [doi]
• Causal inference with outcome-dependent missingness and self-censoringJacob M. Chen, Daniel Malinsky, Rohit Bhattacharya. 358-368 [doi]
• Detection of Short-Term Temporal Dependencies in Hawkes Processes with Heterogeneous Background DynamicsYu Chen, Fengpei Li, Anderson Schneider, Yuriy Nevmyvaka, Asohan Amarasingham, Henry Lam. 369-380 [doi]
• Enhancing Treatment Effect Estimation: A Model Robust Approach Integrating Randomized Experiments and External Controls using the Double Penalty Integration EstimatorYuwen Cheng, Lili Wu, Shu Yang. 381-390 [doi]
• Adaptivity Complexity for Causal Graph DiscoveryDavin Choo, Kirankumar Shiragur. 391-402 [doi]
• Combinatorial categorized bandits with expert rankingsSayak Ray Chowdhury, Gaurav Sinha, Nagarajan Natarajan, Amit Sharma. 403-412 [doi]
• Parity calibrationYoungseog Chung, Aaron Rumack, Chirag Gupta. 413-423 [doi]
• Finite-sample guarantees for Nash Q-learning with linear function approximationPedro Cisneros-Velarde, Sanmi Koyejo. 424-432 [doi]
• Establishing Markov equivalence in cyclic directed graphsTom Claassen, Joris M. Mooij. 433-442 [doi]
• Expectation consistency for calibration of neural networksLucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová. 443-453 [doi]
• Human-in-the-Loop MixupKatherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley C. Love, Adrian Weller. 454-464 [doi]
• Learning to reason about contextual knowledge for planning under uncertaintyCheng Cui, Saeid Amiri, Yan Ding, Xingyue Zhan, Shiqi Zhang 0001. 465-475 [doi]
• Blackbox optimization of unimodal functionsAshok Cutkosky, Abhimanyu Das, Weihao Kong, Chansoo Lee, Rajat Sen. 476-484 [doi]
• Conditional abstraction trees for sample-efficient reinforcement learningMehdi Dadvar, Rashmeet Kaur Nayyar, Siddharth Srivastava 0001. 485-495 [doi]
• Improvable Gap Balancing for Multi-Task LearningYanqi Dai, Nanyi Fei, Zhiwu Lu 0001. 496-506 [doi]
• CrysMMNet: Multimodal Representation for Crystal Property PredictionKishalay Das, Pawan Goyal 0002, Seung-Cheol Lee, Satadeep Bhattacharjee, Niloy Ganguly. 507-517 [doi]
• Dirichlet Proportions Model for Hierarchically Coherent Probabilistic ForecastingAbhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen. 518-528 [doi]
• Neural probabilistic logic programming in discrete-continuous domainsLennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra, Angelika Kimmig, Luc De Raedt. 529-538 [doi]
• Studying the Effect of GNN Spatial Convolutions On The Embedding Space's GeometryClaire Donnat, Sowon Jeong. 539-548 [doi]
• Deep Gaussian mixture ensemblesYousef El-Laham, Niccolò Dalmasso, Elizabeth Fons, Svitlana Vyetrenko. 549-559 [doi]
• Personalized federated domain adaptation for item-to-item recommendationZiwei Fan, Hao Ding 0003, Anoop Deoras, Trong Nghia Hoang. 560-570 [doi]
• Generating Synthetic Datasets by Interpolating along Generalized GeodesicsJiaoJiao Fan, David Alvarez-Melis. 571-581 [doi]
• Logit-based ensemble distribution distillation for robust autoregressive sequence uncertaintiesYassir Fathullah, Guoxuan Xia, Mark J. F. Gales. 582-591 [doi]
• On the role of model uncertainties in Bayesian optimisationJonathan Foldager, Mikkel Jordahn, Lars Kai Hansen, Michael Riis Andersen. 592-601 [doi]
• Does Momentum Help in Stochastic Optimization? A Sample Complexity AnalysisSwetha Ganesh, Rohan Deb, Gugan Thoppe, Amarjit Budhiraja. 602-612 [doi]
• Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and PredictionChengmin Gao, Bin Li 0015. 613-623 [doi]
• Information theoretic clustering via divergence maximization among clustersSahil Garg, Mina Dalirrooyfard, Anderson Schneider, Yeshaya Adler, Yuriy Nevmyvaka, Yu Chen, Fengpei Li, Guillermo A. Cecchi. 624-634 [doi]
• In- or out-of-distribution detection via dual divergence estimationSahil Garg, Sanghamitra Dutta, Mina Dalirrooyfard, Anderson Schneider, Yuriy Nevmyvaka. 635-646 [doi]
• A Data-Driven State Aggregation Approach for Dynamic Discrete Choice ModelsSinong Geng, Houssam Nassif, Carlos A. Manzanares. 647-657 [doi]
• Quasi-Bayesian nonparametric density estimation via autoregressive predictive updatesSahra Ghalebikesabi, Chris C. Holmes, Edwin Fong, Brieuc Lehmann. 658-668 [doi]
• Copula-based deep survival models for dependent censoringAli Hossein Gharari Foomani, Michael Cooper, Russell Greiner, Rahul G. Krishnan. 669-680 [doi]
• Probabilistically robust conformal predictionSubhankar Ghosh, Yuanjie Shi, Taha Belkhouja, Yan Yan 0006, Jana Doppa, Brian Jones. 681-690 [doi]
• Fast and scalable score-based kernel calibration testsPierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton. 691-700 [doi]
• Vacant holes for unsupervised detection of the outliers in compact latent representationMisha Glazunov, Apostolis Zarras. 701-711 [doi]
• Piecewise Deterministic Markov Processes for Bayesian Neural NetworksEthan Goan, Dimitri Perrin, Kerrie L. Mengersen, Clinton Fookes. 712-722 [doi]
• A scalable Walsh-Hadamard regularizer to overcome the low-degree spectral bias of neural networksAli Gorji, Andisheh Amrollahi, Andreas Krause 0001. 723-733 [doi]
• Stochastic Graphical Bandits with Heavy-Tailed RewardsYutian Gou, Jinfeng Yi, Lijun Zhang 0005. 734-744 [doi]
• Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing FlowDenis A. Gudovskiy, Tomoyuki Okuno, Yohei Nakata. 745-755 [doi]
• Functional causal Bayesian optimizationLimor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa. 756-765 [doi]
• Causal Discovery for time series from multiple datasets with latent contextsWiebke Günther, Urmi Ninad, Jakob Runge. 766-776 [doi]
• Sufficient identification conditions and semiparametric estimation under missing not at random mechanismsAnna Guo, Jiwei Zhao, Razieh Nabi. 777-787 [doi]
• Interpretable differencing of machine learning modelsSwagatam Haldar, Diptikalyan Saha, Dennis Wei, Rahul Nair, Elizabeth M. Daly. 788-797 [doi]
• Differentiable user modelsAlex Hämäläinen, Mustafa Mert Çelikok, Samuel Kaski. 798-808 [doi]
• On the Convergence of Continual Learning with Adaptive MethodsSeungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee 0001. 809-818 [doi]
• Revisiting Bayesian network learning with small vertex coverJuha Harviainen, Mikko Koivisto. 819-828 [doi]
• On inference and learning with probabilistic generating circuitsJuha Harviainen, Vaidyanathan Peruvemba Ramaswamy, Mikko Koivisto. 829-838 [doi]
• Inference and sampling of point processes from diffusion excursionsAli-Hasan, Yu Chen, Yuting Ng, Mohamed Abdelghani, Anderson Schneider, Vahid Tarokh. 839-848 [doi]
• Loosely consistent emphatic temporal-difference learningJiamin He, Fengdi Che, Yi Wan, A. Rupam Mahmood. 849-859 [doi]
• Scalable and robust tensor ring decomposition for large-scale dataYicong He, George K. Atia. 860-869 [doi]
• Massively parallel reweighted wake-sleepThomas Heap, Gavin Leech, Laurence Aitchison. 870-878 [doi]
• Increasing effect sizes of pairwise conditional independence tests between random vectorsTom Hochsprung, Jonas Wahl, Andreas Gerhardus, Urmi Ninad, Jakob Runge. 879-889 [doi]
• Optimistic Thompson Sampling-based algorithms for episodic reinforcement learningBingshan Hu, Tianyue H. Zhang, Nidhi Hegde 0001, Mark Schmidt. 890-899 [doi]
• ASTRA: Understanding the practical impact of robustness for probabilistic programsZixin Huang, Saikat Dutta 0001, Sasa Misailovic. 900-910 [doi]
• A near-optimal high-probability swap-Regret upper bound for multi-agent bandits in unknown general-sum gamesZhiming Huang 0002, Jianping Pan 0001. 911-921 [doi]
• Posterior sampling-based online learning for the stochastic shortest path modelMehdi Jafarnia-Jahromi, Liyu Chen, Rahul Jain 0002, Haipeng Luo. 922-931 [doi]
• Investigating a Generalization of Probabilistic Material Implication and Bayesian ConditionalsMichael Jahn, Matthias Scheutz. 932-940 [doi]
• Robust statistical comparison of random variables with locally varying scale of measurementChristoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann, Thomas Augustin. 941-952 [doi]
• Noisy adversarial representation learning for effective and efficient image obfuscationJonghu Jeong, Minyong Cho, Philipp Benz, Tae Hoon Kim. 953-962 [doi]
• Incentivising Diffusion while Preserving Differential PrivacyFengjuan Jia, Mengxiao Zhang, Jiamou Liu, Bakh Khoussainov. 963-972 [doi]
• Content Sharing Design for Social Welfare in Networked Disclosure GameFeiran Jia, Chenxi Qiu, Sarah Rajtmajer, Anna Cinzia Squicciarini. 973-983 [doi]
• Multi-view graph contrastive learning for solving vehicle routing problemsYuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang 0002. 984-994 [doi]
• Bayesian inference for vertex-series-parallel partial ordersChuxuan Jiang, Geoff K. Nicholls, Jeong-Eun (Kate) Lee. 995-1004 [doi]
• Nyström M-Hilbert-Schmidt independence criterionFlorian Kalinke, Zoltán Szabó. 1005-1015 [doi]
• Causal Discovery with Hidden Confounders using the Algorithmic Markov ConditionDavid Kaltenpoth, Jilles Vreeken. 1016-1026 [doi]
• Heavy-tailed linear bandit with Huber regressionMinhyun Kang, Gi-Soo Kim. 1027-1036 [doi]
• Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated LearningBelhal Karimi, Ping Li 0001, Xiaoyun Li. 1037-1046 [doi]
• Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive InterventionsKarine Karine, Predrag V. Klasnja, Susan A. Murphy, Benjamin M. Marlin. 1047-1057 [doi]
• How to use dropout correctly on residual networks with batch normalizationBum-Jun Kim, Hyeyeon Choi, Hyeonah Jang, Donggeon Lee, Sang-Woo Kim. 1058-1067 [doi]
• Phase-shifted adversarial trainingYeachan Kim, Seongyeon Kim, Ihyeok Seo, Bonggun Shin. 1068-1077 [doi]
• On Identifiability of Conditional Causal EffectsYaroslav Kivva, Jalal Etesami, Negar Kiyavash. 1078-1086 [doi]
• Causal effect estimation from observational and interventional data through matrix weighted linear estimatorsKlaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach. 1087-1097 [doi]
• Universal Graph Contrastive Learning with a Novel Laplacian PerturbationTaewook Ko, Yoonhyuk Choi, Chong-kwon Kim. 1098-1108 [doi]
• Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution ShiftingDeqian Kong, Bo Pang, Tian Han 0001, Ying Nian Wu. 1109-1120 [doi]
• Reward-machine-guided, self-paced reinforcement learningCevahir Köprülü, Ufuk Topcu. 1121-1131 [doi]
• Risk-aware curriculum generation for heavy-tailed task distributionsCevahir Köprülü, Thiago D. Simão, Nils Jansen 0001, Ufuk Topcu. 1132-1142 [doi]
• Differentially private synthetic data using KD-treesEleonora Kreacic, Navid Nouri, Vamsi K. Potluru, Tucker Balch, Manuela Veloso. 1143-1153 [doi]
• Optimal Budget Allocation for Crowdsourcing Labels for GraphsAdithya Kulkarni, Mohna Chakraborty, Sihong Xie, Qi Li 0012. 1154-1163 [doi]
• Fixed-Budget Best-Arm Identification with Heterogeneous Reward VariancesAnusha Lalitha, Kousha Kalantari, Yifei Ma, Anoop Deoras, Branislav Kveton. 1164-1173 [doi]
• Variable importance matching for causal inferenceQuinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page. 1174-1184 [doi]
• Towards better certified segmentation via diffusion modelsOthmane Laousy, Alexandre Araujo, Guillaume Chassagnon, Marie-Pierre Revel, Siddharth Garg, Farshad Khorrami, Maria Vakalopoulou. 1185-1195 [doi]
• Finding Invariant Predictors Efficiently via Causal StructureKenneth Lee, Md Musfiqur Rahman, Murat Kocaoglu. 1196-1206 [doi]
• When are post-hoc conceptual explanations identifiable?Tobias Leemann, Michael Kirchhof, Yao Rong, Enkelejda Kasneci, Gjergji Kasneci. 1207-1218 [doi]
• Memory Mechanism for Unsupervised Anomaly DetectionJiahao Li, Yiqiang Chen, Yunbing Xing. 1219-1229 [doi]
• Nonconvex stochastic scaled gradient descent and generalized eigenvector problemsChris Junchi Li, Michael I. Jordan. 1230-1240 [doi]
• Gaussian Process Surrogate Models for Neural NetworksMichael Y. Li, Erin Grant, Thomas L. Griffiths 0001. 1241-1252 [doi]
• CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language ModelsJiazheng Li 0002, Zhaoyue Sun, Bin Liang, Lin Gui 0003, Yulan He 0001. 1253-1262 [doi]
• BISCUIT: Causal Representation Learning from Binary InteractionsPhillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves. 1263-1273 [doi]
• Accelerating Voting by Quantum ComputationAo Liu, Qishen Han, Lirong Xia, Nengkun Yu. 1274-1283 [doi]
• Residual-based error bound for physics-informed neural networksShuheng Liu, Xiyue Huang, Pavlos Protopapas. 1284-1293 [doi]
• No-Regret Linear Bandits beyond RealizabilityChong Liu, Ming Yin, Yu-Xiang Wang. 1294-1303 [doi]
• Benefits of monotonicity in safe exploration with Gaussian processesArpan Losalka, Jonathan Scarlett. 1304-1314 [doi]
• Practical privacy-preserving Gaussian process regression via secret sharingJinglong Luo, Yehong Zhang, Jiaqi Zhang, Shuang Qin, Hui Wang, Yue Yu, Zenglin Xu. 1315-1325 [doi]
• DeepGD3: Unknown-Aware Deep Generative/Discriminative Hybrid Defect Detector for PCB Soldering InspectionChing-Wen Ma, Yanwei Liu. 1326-1335 [doi]
• Federated learning of models pre-trained on different features with consensus graphsTengfei Ma, Trong Nghia Hoang, Jie Chen. 1336-1346 [doi]
• Random Reshuffling with Variance Reduction: New Analysis and Better RatesGrigory Malinovsky, Alibek Sailanbayev, Peter Richtárik. 1347-1357 [doi]
• The Shrinkage-Delinkage Trade-off: an Analysis of Factorized Gaussian Approximations for Variational InferenceCharles C. Margossian, Lawrence K. Saul. 1358-1367 [doi]
• Partial identification of dose responses with hidden confoundersMyrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg. 1368-1379 [doi]
• Knowledge Intensive Learning of Cutset NetworksSaurabh Mathur 0002, Vibhav Gogate, Sriraam Natarajan. 1380-1389 [doi]
• TCE: A Test-Based Approach to Measuring Calibration ErrorTakuo Matsubara, Niek Tax, Richard Mudd, Ido Guy. 1390-1400 [doi]
• Causal information splitting: Engineering proxy features for robustness to distribution shiftsBijan Mazaheri, Atalanti-Anastasia Mastakouri, Dominik Janzing, Michaela Hardt. 1401-1411 [doi]
• KrADagrad: Kronecker approximation-domination gradient preconditioned stochastic optimizationJonathan Mei, Alexander Moreno, Luke Walters. 1412-1422 [doi]
• On the Relation between Policy Improvement and Off-Policy Minimum-Variance Policy EvaluationAlberto Maria Metelli, Samuele Meta, Marcello Restelli. 1423-1433 [doi]
• On Minimizing the Impact of Dataset Shifts on Actionable ExplanationsAnna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju. 1434-1444 [doi]
• Adaptive Conditional Quantile Neural ProcessesPeiman Mohseni, Nick Duffield, Bani K. Mallick, Arman Hasanzadeh. 1445-1455 [doi]
• Composing Efficient, Robust Tests for Policy SelectionDustin Morrill, Thomas J. Walsh 0001, Daniel Hernandez, Peter R. Wurman, Peter Stone. 1456-1466 [doi]
• On Testability and Goodness of Fit Tests in Missing Data ModelsRazieh Nabi, Rohit Bhattacharya. 1467-1477 [doi]
• Active metric learning and classification using similarity queriesNamrata Nadagouda, Austin Xu, Mark A. Davenport. 1478-1488 [doi]
• Two-phase Attacks in Security GamesAndrzej Nagórko, Pawel Ciosmak, Tomasz P. Michalak. 1489-1498 [doi]
• Keep-Alive Caching for the Hawkes processSushirdeep Narayana, Ian A. Kash. 1499-1509 [doi]
• Simple Transferability Estimation for Regression TasksCuong N. Nguyen, Phong Tran, Lam Si Tung Ho, Vu C. Dinh, Anh T. Tran, Tal Hassner, Cuong V. Nguyen. 1510-1521 [doi]
• Probabilistic Multi-Dimensional ClassificationVu-Linh Nguyen, Yang Yang, Cassio de Campos. 1522-1533 [doi]
• Efficient Failure Pattern Identification of Predictive AlgorithmsBao Nguyen, Viet Anh Nguyen. 1534-1544 [doi]
• Size-constrained k-submodular maximization in near-linear timeGuanyu Nie, Yanhui Zhu, Yididiya Y. Nadew, Samik Basu, A. Pavan, Christopher John Quinn. 1545-1554 [doi]
• An improved variational approximate posterior for the deep Wishart processSebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison. 1555-1563 [doi]
• Incentivizing honest performative predictions with proper scoring rulesCaspar Oesterheld, Johannes Treutlein, Emery Cooper, Rubi Hudson. 1564-1574 [doi]
• Graph classification Gaussian processes via spectral featuresFelix L. Opolka, Yin-Cong Zhi, Pietro Liò, Xiaowen Dong 0001. 1575-1585 [doi]
• Approximate Thompson Sampling via Epistemic Neural NetworksIan Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy. 1586-1595 [doi]
• Structure-aware robustness certificates for graph classificationPierre Osselin, Henry Kenlay, Xiaowen Dong 0001. 1596-1605 [doi]
• Baysian numerical integration with neural networksKatharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol. 1606-1617 [doi]
• Maximizing submodular functions under submodular constraintsMadhavan R. Padmanabhan, Yanhui Zhu, Samik Basu 0001, Aduri Pavan. 1618-1627 [doi]
• Stochastic Generative Flow NetworksLing Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio. 1628-1638 [doi]
• Multi-View Independent Component Analysis with Shared and Individual SourcesTeodora Pandeva, Patrick Forré. 1639-1650 [doi]
• Copula for Instance-wise Feature Selection and RankHanyu Peng, Guanhua Fang, Ping Li 0001. 1651-1661 [doi]
• Boosting AND/OR-based computational protein design: dynamic heuristics and generalizable UFOBobak Pezeshki, Radu Marinescu 0002, Alexander Ihler, Rina Dechter. 1662-1672 [doi]
• Exact Count of Boundary Pieces of ReLU Classifiers: Towards the Proper Complexity Measure for ClassificationPawel Piwek, Adam Klukowski, Tianyang Hu. 1673-1683 [doi]
• Split, count, and share: a differentially private set intersection cardinality estimation protocolMichael Purcell, Yang Li, Kee Siong Ng. 1684-1694 [doi]
• Jana: Jointly amortized neural approximation of complex Bayesian modelsStefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner. 1695-1706 [doi]
• USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolutionVikrant Rangnekar, Uddeshya Upadhyay, Zeynep Akata, Biplab Banerjee. 1707-1717 [doi]
• Contrastive learning for supervised graph matchingGathika Ratnayaka, Qing Wang, Yang Li. 1718-1729 [doi]
• Validation of composite systems by discrepancy propagationDavid Reeb, Kanil Patel, Karim Said Barsim, Martin Schiegg, Sebastian Gerwinn. 1730-1740 [doi]
• Inference for probabilistic dependency graphsOliver E. Richardson, Joseph Y. Halpern, Christopher De Sa. 1741-1751 [doi]
• The past does matter: correlation of subsequent states in trajectory predictions of Gaussian Process modelsSteffen Ridderbusch, Sina Ober-Blöbaum, Paul Goulart. 1752-1761 [doi]
• Approximately Bayes-optimal pseudo-label selectionJulian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin. 1762-1773 [doi]
• Hallucinated adversarial control for conservative offline policy evaluationJonas Rothfuss, Bhavya Sukhija, Tobias Birchler, Parnian Kassraie, Andreas Krause 0001. 1774-1784 [doi]
• Semi-supervised learning of partial differential operators and dynamical flowsMichael Rotman, Amit Dekel, Ran Ilan Ber, Lior Wolf, Yaron Oz. 1785-1794 [doi]
• Is the volume of a credal set a good measure for epistemic uncertainty?Yusuf Sale, Michele Caprio, Eyke Hüllermeier. 1795-1804 [doi]
• Heteroskedastic Geospatial Tracking with Distributed Camera NetworksColin Samplawski, Shiwei Fang, Ziqi Wang 0001, Deepak Ganesan, Mani B. Srivastava, Benjamin M. Marlin. 1805-1814 [doi]
• Online Heavy-tailed Change-point detectionAbishek Sankararaman, Balakrishnan Narayanaswamy. 1815-1826 [doi]
• Learning good interventions in causal graphs via coveringAyush Sawarni, Rahul Madhavan, Gaurav Sinha, Siddharth Barman. 1827-1836 [doi]
• Local Message Passing on Frustrated SystemsLuca Schmid, Joshua Brenk, Laurent Schmalen. 1837-1846 [doi]
• Lifelong bandit optimization: no prior and no regretFelix Schur, Parnian Kassraie, Jonas Rothfuss, Andreas Krause 0001. 1847-1857 [doi]
• Learning from Low Rank Tensor Data: A Random Tensor Theory PerspectiveMohamed-El-Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Guillaud. 1858-1867 [doi]
• MDPose: real-time multi-person pose estimation via mixture density modelSeunghyeon Seo, Jaeyoung Yoo, Jihye Hwang, Nojun Kwak. 1868-1878 [doi]
• Mnemonist: Locating Model Parameters that Memorize Training ExamplesAli Shahin Shamsabadi, Jamie Hayes, Borja Balle, Adrian Weller. 1879-1888 [doi]
• Implicit Training of Inference Network Models for Structured PredictionShiv Shankar. 1889-1899 [doi]
• Efficiently learning the graph for semi-supervised learningDravyansh Sharma, Maxwell Jones. 1900-1910 [doi]
• Counting Background Knowledge Consistent Markov Equivalent Directed Acyclic GraphsVidya Sagar Sharma. 1911-1920 [doi]
• SymNet 3.0: Exploiting Long-Range Influences in Learning Generalized Neural Policies for Relational MDPsVishal Sharma, Daman Arora, Mausam, Parag Singla. 1921-1931 [doi]
• Risk-limiting financial audits via weighted sampling without replacementShubhanshu Shekhar, Ziyu Xu, Zachary C. Lipton, Pierre J. Liang, Aaditya Ramdas. 1932-1941 [doi]
• Learning Nonlinear Causal Effect via Kernel Anchor RegressionWenqi Shi, Wenkai Xu. 1942-1952 [doi]
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