


default search action
28th AISTATS 2025: Mai Khao, Thailand
- Yingzhen Li, Stephan Mandt, Shipra Agrawal, Mohammad Emtiyaz Khan:

International Conference on Artificial Intelligence and Statistics, AISTATS 2025, Mai Khao, Thailand, 3-5 May 2025. Proceedings of Machine Learning Research 258, PMLR 2025 - Rickmer Schulte, David Rügamer:

Additive Model Boosting: New Insights and Path(ologie)s. 1-9 - Daniel Dold, Julius Kobialka, Nicolai Palm, Emanuel Sommer, David Rügamer, Oliver Dürr:

Paths and Ambient Spaces in Neural Loss Landscapes. 10-18 - Vincent Blot, Anastasios Nikolas Angelopoulos, Michael I. Jordan, Nicolas J.-B. Brunel:

Automatically Adaptive Conformal Risk Control. 19-27 - Ayush Bharti, Daolang Huang, Samuel Kaski, François-Xavier Briol:

Cost-aware simulation-based inference. 28-36 - Aramayis Dallakyan, Yang Ni:

Generalized Criterion for Identifiability of Additive Noise Models Using Majorization. 37-45 - Shinsaku Sakaue, Han Bao, Taira Tsuchiya:

Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel-Young Loss Perspective and Gap-Dependent Regret Analysis. 46-54 - Yuheng Ma, Ke Jia, Hanfang Yang:

Locally Private Estimation with Public Features. 55-63 - Takahiro Kawashima, Hideitsu Hino:

A Family of Distributions of Random Subsets for Controlling Positive and Negative Dependence. 64-72 - Xu Cai, Jonathan Scarlett:

Lower Bounds for Time-Varying Kernelized Bandits. 73-81 - Ruihan Xu, Yiping Lu:

Randomized Iterative Solver as Iterative Refinement: A Simple Fix Towards Backward Stability. 82-90 - Giora Simchoni, Saharon Rosset:

Flexible Copula-Based Mixed Models in Deep Learning: A Scalable Approach to Arbitrary Marginals. 91-99 - Mikolaj Slupinski:

Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems. 100-108 - Saeyoung Rho, Andrew Tang, Noah Bergam, Rachel Cummings, Vishal Misra:

ClusterSC: Advancing Synthetic Control with Donor Selection. 109-117 - Kalinin Nikita, Lukas Steinberger:

Efficient Estimation of a Gaussian Mean with Local Differential Privacy. 118-126 - Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet:

Credal Two-Sample Tests of Epistemic Uncertainty. 127-135 - Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba:

Bayesian Off-Policy Evaluation and Learning for Large Action Spaces. 136-144 - Tim Rensmeyer, Oliver Niggemann:

On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors. 145-153 - Tim G. J. Rudner, Xiang Pan, Yucen Lily Li, Ravid Shwartz-Ziv, Andrew Gordon Wilson:

Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable. 154-162 - Anshul Thakur, Soheila Molaei, Patrick Schwab, Danielle Belgrave, Kim Branson, David A. Clifton:

Optimising Clinical Federated Learning through Mode Connectivity-based Model Aggregation. 163-171 - Shpresim Sadiku, Moritz Wagner, Sai Ganesh Nagarajan, Sebastian Pokutta:

S-CFE: Simple Counterfactual Explanations. 172-180 - Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L. Borsa, Arthur Guez, Will Dabney:

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. 181-189 - Peter Matthew Jacobs, Anirban Bhattacharya, Debdeep Pati, Lekha Patel, Jeff M. Phillips:

Estimation of Large Zipfian Distributions with Sort and Snap. 190-198 - Nandi Schoots, Mattia Jacopo Villani, Niels uit de Bos:

Relating Piecewise Linear Kolmogorov Arnold Networks to ReLU Networks. 199-207 - Madhumitha Shridharan, Garud Iyengar:

β-th order Acyclicity Derivatives for DAG Learning. 208-216 - Guojun Zhu, Sanguo Zhang, Mingyang Ren:

Conditional Generative Learning from Invariant Representations in Multi-Source: Robustness and Efficiency. 217-225 - Mitsuhiro Fujikawa, Youhei Akimoto, Jun Sakuma, Kazuto Fukuchi:

Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift. 226-234 - Jian Xu, Shian Du, Junmei Yang, Xinghao Ding, Delu Zeng, John Paisley:

Bayesian Gaussian Process ODEs via Double Normalizing Flows. 235-243 - Edmund Lau, Zach Furman, George Wang, Daniel Murfet, Susan Wei:

The Local Learning Coefficient: A Singularity-Aware Complexity Measure. 244-252 - Masanari Kimura, Howard D. Bondell:

Density Ratio Estimation via Sampling along Generalized Geodesics on Statistical Manifolds. 253-261 - Chenhan Fu, Guoming Wang, Juncheng Li, Rongxing Lu, Siliang Tang:

Choice is what matters after Attention. 262-270 - Mathieu Besançon, Sebastian Pokutta, Elias Samuel Wirth:

The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size Control. 271-279 - David Martínez-Rubio, Christophe Roux, Christopher Criscitiello, Sebastian Pokutta:

Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties. 280-288 - Zongrui Zou, Jingcheng Liu, Jalaj Upadhyay:

Almost linear time differentially private release of synthetic graphs. 289-297 - Yue Xing:

Adversarial Training in High-Dimensional Regression: Generated Data and Neural Networks. 298-306 - Ossi Räisä, Antti Honkela:

A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets. 307-315 - Alex Barbier-Chebbah, Christian L. Vestergaard, Jean-Baptiste Masson, Etienne Boursier:

Approximate information maximization for bandit games. 316-324 - Abhinav Agrawal, Justin Domke:

Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VI. 325-333 - Peiyuan Zhang, Jiaye Teng, Jingzhao Zhang:

Generalization Lower Bounds for GD and SGD in Smooth Stochastic Convex Optimization. 334-342 - Sihan Zeng, Sujay Bhatt, Alec Koppel, Sumitra Ganesh:

Learning in Herding Mean Field Games: Single-Loop Algorithm with Finite-Time Convergence Analysis. 343-351 - Fabio Feser, Marina Evangelou:

Strong Screening Rules for Group-based SLOPE Models. 352-360 - Jaehyun Park, Junyeop Kwon, Dabeen Lee:

Infinite-Horizon Reinforcement Learning with Multinomial Logit Function Approximation. 361-369 - Diantong Li, Fengxue Zhang, Chong Liu, Yuxin Chen:

Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation. 370-378 - Nicolas Nguyen, Imad Aouali, András György, Claire Vernade:

Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits. 379-387 - Florian Kalinke, Zoltán Szabó, Bharath K. Sriperumbudur:

Nyström Kernel Stein Discrepancy. 388-396 - Achraf Azize, Debabrota Basu:

Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership Leakage. 397-405 - Shogo Iwazaki, Shion Takeno:

Near-Optimal Algorithm for Non-Stationary Kernelized Bandits. 406-414 - Shogo Iwazaki, Tomohiko Tanabe, Mitsuru Irie, Shion Takeno, Kota Matsui, Yu Inatsu:

No-Regret Bayesian Optimization with Stochastic Observation Failures. 415-423 - Yujia Zheng, Yang Liu, Jiaxiong Yao, Yingyao Hu, Kun Zhang:

Nonparametric Factor Analysis and Beyond. 424-432 - Deep Chakraborty, Yann LeCun, Tim G. J. Rudner, Erik G. Learned-Miller:

Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization. 433-441 - Manan Saxena, Tinghua Chen, Justin D. Silverman:

Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models. 442-450 - Han Bao, Shinsaku Sakaue:

Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States. 451-459 - Tuan Nguyen, Jay Barrett, Kwang-Sung Jun:

HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree Search. 460-468 - Minsu Kim, Sanghyeok Choi, Hyeonah Kim, Jiwoo Son, Jinkyoo Park, Yoshua Bengio:

Ant Colony Sampling with GFlowNets for Combinatorial Optimization. 469-477 - Lucia Pezzetti, Stefano Favaro, Stefano Peluchetti:

Function-Space MCMC for Bayesian Wide Neural Networks. 478-486 - Chungpa Lee, Jeongheon Oh, Kibok Lee, Jy-yong Sohn:

A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning. 487-495 - Nikolaos Nakis, Chrysoula Kosma, Giannis Nikolentzos, Michail Chatzianastasis, Iakovos Evdaimon, Michalis Vazirgiannis:

Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings. 496-504 - Benedikt Fesl, Benedikt Böck, Florian Strasser, Michael Baur, Michael Joham, Wolfgang Utschick:

On the Asymptotic Mean Square Error Optimality of Diffusion Models. 505-513 - Yang Luo, Michael J. O'Neill:

Adaptive Extragradient Methods for Root-finding Problems under Relaxed Assumptions. 514-522 - Chenyang Li, Yingyu Liang, Zhenmei Shi, Zhao Song, Tianyi Zhou:

Fourier Circuits in Neural Networks and Transformers: A Case Study of Modular Arithmetic with Multiple Inputs. 523-531 - Marta Campi, Guillaume Staerman, Gareth W. Peters, Tomoko Masui:

Signature Isolation Forest. 532-540 - Tao Wen, Zihan Wang, Quan Zhang, Qi Lei:

Elastic Representation: Mitigating Spurious Correlations for Group Robustness. 541-549 - Zhaolin Ren, Runyu Zhang, Bo Dai, Na Li:

Scalable spectral representations for multiagent reinforcement learning in network MDPs. 550-558 - Ziliang Samuel Zhong, Xiang Pan, Qi Lei:

Bridging Domains with Approximately Shared Features. 559-567 - Futoshi Futami:

Epistemic Uncertainty and Excess Risk in Variational Inference. 568-576 - Jiaru Zhang, Rui Ding, Qiang Fu, Bojun Huang, Zizhen Deng, Yang Hua, Haibing Guan, Shi Han, Dongmei Zhang:

Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning. 577-585 - Yucheng Liu, Xiaodong Li:

Selecting the Number of Communities for Weighted Degree-Corrected Stochastic Block Models. 586-594 - Siyao Wang, Miles E. Lopes:

Empirical Error Estimates for Graph Sparsification. 595-603 - Vahid Shahverdi, Giovanni Luca Marchetti, Kathlén Kohn:

On the Geometry and Optimization of Polynomial Convolutional Networks. 604-612 - Sheng Liu, Zihan Wang, Yuxiao Chen, Qi Lei:

Data Reconstruction Attacks and Defenses: A Systematic Evaluation. 613-621 - Behnoosh Zamanlooy, Mario Díaz, Shahab Asoodeh:

Locally Private Sampling with Public Data. 622-630 - Zexuan Sun, Garvesh Raskutti:

Reliable and Scalable Variable Importance Estimation via Warm-start and Early Stopping. 631-639 - Congye Wang, Wilson Ye Chen, Heishiro Kanagawa, Chris J. Oates:

Reinforcement Learning for Adaptive MCMC. 640-648 - Zheyang Shen, Jeremias Knoblauch, Samuel Power, Chris J. Oates:

Prediction-Centric Uncertainty Quantification via MMD. 649-657 - Daiqi Gao, Hsin-Yu Lai, Predrag Klasnja, Susan A. Murphy:

Harnessing Causality in Reinforcement Learning with Bagged Decision Times. 658-666 - Wonyoung Kim, Garud Iyengar, Assaf Zeevi:

Learning the Pareto Front Using Bootstrapped Observation Samples. 667-675 - Jong-Ik Park, Srinivasa Pranav, José M. F. Moura, Carlee Joe-Wong:

FedBaF: Federated Learning Aggregation Biased by a Foundation Model. 676-684 - Chungpa Lee, Jongho Im, Joseph H. T. Kim:

A Generalized Theory of Mixup for Structure-Preserving Synthetic Data. 685-693 - Oguz Kaan Yüksel, Nicolas Flammarion:

On the Sample Complexity of Next-Token Prediction. 694-702 - Paul Edmund Chang, Nasrulloh Ratu Bagus Satrio Loka, Daolang Huang, Ulpu Remes, Samuel Kaski, Luigi Acerbi:

Amortized Probabilistic Conditioning for Optimization, Simulation and Inference. 703-711 - Leo Widmer, Jiawei Huang, Niao He:

Steering No-Regret Agents in MFGs under Model Uncertainty. 712-720 - Zijun Gao, Shu Ge, Jian Qian:

Bridging Multiple Worlds: Multi-marginal Optimal Transport for Causal Partial-identification Problem. 721-729 - Ruofeng Yang, Bo Jiang, Shuai Li:

The Polynomial Iteration Complexity for Variance Exploding Diffusion Models: Elucidating SDE and ODE Samplers. 730-738 - Yatong Chen, Andrew Estornell, Yevgeniy Vorobeychik, Yang Liu:

To Give or Not to Give? The Impacts of Strategically Withheld Recourse. 739-747 - Sunmin Oh, Seungsu Han, Gunwoong Park:

Optimal estimation of linear non-Gaussian structure equation models. 748-756 - Gaëtan Serré, Argyris Kalogeratos, Nicolas Vayatis:

Stein Boltzmann Sampling: A Variational Approach for Global Optimization. 757-765 - Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier, Brian Staber:

Learning signals defined on graphs with optimal transport and Gaussian process regression. 766-774 - Elizabeth Louise Baker, Moritz Schauer, Stefan Sommer:

Score matching for bridges without learning time-reversals. 775-783 - Abdullah Tokmak, Kiran G. Krishnan, Thomas B. Schön, Dominik Baumann:

Safe exploration in reproducing kernel Hilbert spaces. 784-792 - Rohan Ghosh, Mehul Motani:

Ordered V-information Growth: A Fresh Perspective on Shared Information. 793-801 - Ziyad Benomar, Vianney Perchet:

On Tradeoffs in Learning-Augmented Algorithms. 802-810 - Jim Zhao, Nikita Doikov, Aurélien Lucchi:

Cubic regularized subspace Newton for non-convex optimization. 811-819 - David R. Burt, Yunyi Shen, Tamara Broderick:

Consistent Validation for Predictive Methods in Spatial Settings. 820-828 - Hung-Hsu Chou, Johannes Maly, Claudio Mayrink Verdun, Bernardo Freitas Paulo da Costa, Heudson Mirandola:

Get rid of your constraints and reparametrize: A study in NNLS and implicit bias. 829-837 - Alejandro D. de la Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos:

Collaborative non-parametric two-sample testing. 838-846 - Iosif Lytras, Panayotis Mertikopoulos:

Tamed Langevin sampling under weaker conditions. 847-855 - Yannick Eich, Bastian Alt, Heinz Koeppl:

Entropic Matching for Expectation Propagation of Markov Jump Processes. 856-864 - Yves Rychener, Daniel Kuhn, Yifan Hu:

Global Group Fairness in Federated Learning via Function Tracking. 865-873 - Aya Kayal, Sattar Vakili, Laura Toni, Alberto Bernacchia:

Near-Optimal Sample Complexity in Reward-Free Kernel-based Reinforcement Learning. 874-882 - Matthieu Carreau, Roi Naveiro, William N. Caballero:

Poisoning Bayesian Inference via Data Deletion and Replication. 883-891 - Róbert Istvan Busa-Fekete, Umar Syed:

Near-optimal algorithms for private estimation and sequential testing of collision probability. 892-900 - Somnath Basu Roy Chowdhury, Kumar Avinava Dubey, Ahmad Beirami, Rahul Kidambi, Nicholas Monath, Amr Ahmed, Snigdha Chaturvedi:

Fundamental Limits of Perfect Concept Erasure. 901-909 - Suqi Liu, Morgane Austern:

Perfect Recovery for Random Geometric Graph Matching with Shallow Graph Neural Networks. 910-918 - Daniele Bracale, Subha Maity, Felipe Maia Polo, Seamus Somerstep, Moulinath Banerjee, Yuekai Sun:

Microfoundation inference for strategic prediction. 919-927 - Keyue Jiang, Bohan Tang, Xiaowen Dong, Laura Toni:

Heterogeneous Graph Structure Learning through the Lens of Data-generating Processes. 928-936 - Yingqian Cui, Jie Ren, Pengfei He, Hui Liu, Jiliang Tang, Yue Xing:

Superiority of Multi-Head Attention: A Theoretical Study in Shallow Transformers in In-Context Linear Regression. 937-945 - Lynn Chua, Badih Ghazi, Charlie Harrison, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang:

Balls-and-Bins Sampling for DP-SGD. 946-954 - Kuldeep S. Meel, Gunjan Kumar, Yash Pote:

Distance Estimation for High-Dimensional Discrete Distributions. 955-963 - Jiaqi Sun, Yujia Zheng, Xinshuai Dong, Haoyue Dai, Kun Zhang:

Type Information-Assisted Self-Supervised Knowledge Graph Denoising. 964-972 - Daniele Bracale, Subha Maity, Yuekai Sun, Moulinath Banerjee:

Learning the Distribution Map in Reverse Causal Performative Prediction. 973-981 - Zhu Wang, Praveen Raj Veluswami, Harsh Mishra, Sathya N. Ravi:

Optimizing Neural Network Training and Quantization with Rooted Logistic Objectives. 982-990 - Nima Akbarzadeh, Yossiri Adulyasak, Erick Delage:

Planning and Learning in Risk-Aware Restless Multi-Arm Bandits. 991-999 - Cyrille Kone, Marc Jourdan, Emilie Kaufmann:

Pareto Set Identification With Posterior Sampling. 1000-1008 - Xuefeng Gao, Lingjiong Zhu:

Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances. 1009-1017 - Xingzhi Sun, Danqi Liao, Kincaid MacDonald, Yanlei Zhang, Guillaume Huguet, Guy Wolf, Ian Adelstein, Tim G. J. Rudner, Smita Krishnaswamy:

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds. 1018-1026 - Junkyu Lee, Tian Gao, Elliot Nelson, Miao Liu, Debarun Bhattacharjya, Songtao Lu:

Q-function Decomposition with Intervention Semantics for Factored Action Spaces. 1027-1035 - Kenghao Zheng, Zi Long, Shuxin Wang:

HAR-former: Hybrid Transformer with an Adaptive Time-Frequency Representation Matrix for Long-Term Series Forecasting. 1036-1044 - Yuta Nakahara, Shota Saito, Naoki Ichijo, Koki Kazama, Toshiyasu Matsushima:

Bayesian Decision Theory on Decision Trees: Uncertainty Evaluation and Interpretability. 1045-1053 - Chengrui Qu, Laixi Shi, Kishan Panaganti, Pengcheng You, Adam Wierman:

Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data. 1054-1062 - Zijun Gao:

Trustworthy assessment of heterogeneous treatment effect estimator via analysis of relative error. 1063-1071 - Elena Grigorescu, Young-San Lin, Maoyuan Song:

Learning-Augmented Algorithms for Online Concave Packing and Convex Covering Problems. 1072-1080 - Dun Zeng, Zenglin Xu, Shiyu Liu, Yu Pan, Qifan Wang, Xiaoying Tang:

On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond. 1081-1089 - Yuxin Wang, Botian Jiang, Yiran Guo, Quan Gan, David Wipf, Xuanjing Huang, Xipeng Qiu:

Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners. 1090-1098 - Gilad Yehudai, Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain:

Locally Optimal Descent for Dynamic Stepsize Scheduling. 1099-1107 - Taole Sha, Michael Minyi Zhang:

Online Student-t Processes with an Overall-local Scale Structure for Modelling Non-stationary Data. 1108-1116 - Andi Nika, Jonathan Nöther, Debmalya Mandal, Parameswaran Kamalaruban, Adish Singla, Goran Radanovic:

Policy Teaching via Data Poisoning in Learning from Human Preferences. 1117-1125 - Julien Aubert, Louis Köhler, Luc Lehéricy, Giulia Mezzadri, Patricia Reynaud-Bouret:

Model selection for behavioral learning data and applications to contextual bandits. 1126-1134 - Lei You, Lele Cao, Mattias Nilsson, Bo Zhao, Lei Lei:

Distributional Counterfactual Explanations With Optimal Transport. 1135-1143 - Jiaqi Han, Mingjian Jiang, Yuxuan Song, Stefano Ermon, Minkai Xu:

f-PO: Generalizing Preference Optimization with f-divergence Minimization. 1144-1152 - Eliot Beyler, Francis Bach:

Variational Inference on the Boolean Hypercube with the Quantum Entropy. 1153-1161 - Zhirui Chen, P. N. Karthik, Yeow Meng Chee, Vincent Y. F. Tan:

Optimal Multi-Objective Best Arm Identification with Fixed Confidence. 1162-1170 - Ashwin Samudre, Mircea Petrache, Brian Nord, Shubhendu Trivedi:

Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks. 1171-1179 - Inioluwa Deborah Raji, Lydia T. Liu:

Evaluating Prediction-based Interventions with Human Decision Makers In Mind. 1180-1188 - Cyrille Kone, Emilie Kaufmann, Laura Richert:

Bandit Pareto Set Identification in a Multi-Output Linear Model. 1189-1197 - Måns Magnusson, Jakob Torgander, Paul-Christian Bürkner, Lu Zhang, Bob Carpenter, Aki Vehtari:

posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms. 1198-1206 - Antônio H. Ribeiro, Thomas B. Schön, Dave Zachariah, Francis Bach:

Efficient Optimization Algorithms for Linear Adversarial Training. 1207-1215 - Guillaume Braun, Minh Ha Quang, Masaaki Imaizumi:

Learning a Single Index Model from Anisotropic Data with Vanilla Stochastic Gradient Descent. 1216-1224 - Angel David Reyero Lobo, Alexis Ayme, Claire Boyer, Erwan Scornet:

A primer on linear classification with missing data. 1225-1233 - Richard Schwank, Andrew McCormack, Mathias Drton:

Robust Score Matching. 1234-1242 - Lorenz Kummer, Wilfried N. Gansterer, Nils Morten Kriege:

On the Relationship Between Robustness and Expressivity of Graph Neural Networks. 1243-1251 - Yuxiong Gao, Wentao Li, Rong Chen:

Parameter estimation in state space models using particle importance sampling. 1252-1260 - Francesco Saverio Pezzicoli, Valentina Ros, François P. Landes, Marco Baity-Jesi:

Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model. 1261-1269 - Nithish Suresh Babu, Ritesh Kumar, Shashank Vatedka:

Unbiased Quantization of the L1 Ball for Communication-Efficient Distributed Mean Estimation. 1270-1278 - Van Khoa Nguyen, Maciej Falkiewicz, Giangiacomo Mercatali, Alexandros Kalousis:

MING: A Functional Approach to Learning Molecular Generative Models. 1279-1287 - Diego Martinez-Taboada, Aaditya Ramdas:

Sequential Kernelized Stein Discrepancy. 1288-1296 - Leonardo Martins Bianco, Christine Keribin, Zacharie Naulet:

SubSearch: Robust Estimation and Outlier Detection for Stochastic Block Models via Subgraph Search. 1297-1305 - Yan Chen, Jose H. Blanchet, Krzysztof Dembczynski, Laura Fee Nern, Aaron E. Flores:

Optimal downsampling for Imbalanced Classification with Generalized Linear Models. 1306-1314 - Safwan Labbi, Daniil Tiapkin, Lorenzo Mancini, Paul Mangold, Eric Moulines:

Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents. 1315-1323 - Kun Zhao, Jiayi Wang, Yifei Lou:

Noisy Low-Rank Matrix Completion via Transformed L1 Regularization and its Theoretical Properties. 1324-1332 - Peyman Morteza:

Representer Theorems for Metric and Preference Learning: Geometric Insights and Algorithms. 1333-1341 - Virginie Loison, Guillaume Staerman, Thomas Moreau:

UNHaP: Unmixing Noise from Hawkes Processes. 1342-1350 - Davin Hill, Joshua T. Bone, Aria Masoomi, Max Torop, Jennifer G. Dy:

Axiomatic Explainer Globalness via Optimal Transport. 1351-1359 - Sanjeeb Dash, Joao Goncalves, Tian Gao:

Integer Programming Based Methods and Heuristics for Causal Graph Learning. 1360-1368 - Andrew Stirn, David A. Knowles:

The VampPrior Mixture Model. 1369-1377 - Asfandyar Azhar, Paul Thielen, Curtis P. Langlotz:

MEDUSA: Medical Data Under Shadow Attacks via Hybrid Model Inversion. 1378-1386 - Bo Yuan, Jiaojiao Fan, Jiaming Liang, Yongxin Chen:

Proximal Sampler with Adaptive Step Size. 1387-1395 - Ananda Theertha Suresh, Andrew Thangaraj, Aditya Nanda Kishore Khandavally:

Rate of Model Collapse in Recursive Training. 1396-1404 - Renpu Liu, Peng Wang, Donghao Li, Cong Shen, Jing Yang:

A Shared Low-Rank Adaptation Approach to Personalized RLHF. 1405-1413 - Giorgio Micali, Clément Lezane, Annika Betken:

Differentially private algorithms for linear queries via stochastic convex optimization. 1414-1422 - Jake Fawkes, Lucile Ter-Minassian, Desi R. Ivanova, Uri Shalit, Christopher C. Holmes:

Is Merging Worth It? Securely Evaluating the Information Gain for Causal Dataset Acquisition. 1423-1431 - Alessandro Mastrototaro, Mathias Müller, Jimmy Olsson:

Recursive Learning of Asymptotic Variational Objectives. 1432-1440 - El Mahdi Chayti, Nikita Doikov, Martin Jaggi:

Improving Stochastic Cubic Newton with Momentum. 1441-1449 - Gil Kur, Aditya Guntuboyina:

Adaptive Convergence Rates for Log-Concave Maximum Likelihood. 1450-1458 - Shuhui Zhu, Baoxiang Wang, Sriram Ganapathi Subramanian, Pascal Poupart:

Learning to Negotiate via Voluntary Commitment. 1459-1467 - Ziwei Su, Diego Klabjan:

Differentiable Calibration of Inexact Stochastic Simulation Models via Kernel Score Minimization. 1468-1476 - Vladimir Braverman, Prathamesh Dharangutte, Shreyas Pai, Vihan Shah, Chen Wang:

Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time. 1477-1485 - Anup Rao, Peng Zhang:

On Distributional Discrepancy for Experimental Design with General Assignment Probabilities. 1486-1494 - Seiyun Shin, Ilan Shomorony, Peter Macgregor:

Dynamic DBSCAN with Euler Tour Sequences. 1495-1503 - Prathamesh Dharangutte, Jie Gao, Ruobin Gong, Guanyang Wang:

Differentially Private Range Queries with Correlated Input Perturbation. 1504-1512 - Alessio Mazzetto, Reza Esfandiarpoor, Akash Singirikonda, Eli Upfal, Stephen H. Bach:

An Adaptive Method for Weak Supervision with Drifting Data. 1513-1521 - Devansh Gupta, Meisam Razaviyayn, Vatsal Sharan:

On the Inherent Privacy of Zeroth-Order Projected Gradient Descent. 1522-1530 - Vishnu Teja Kunde, Vicram Rajagopalan, Chandra Shekhara Kaushik Valmeekam, Krishna Narayanan, Jean-François Chamberland, Dileep Kalathil, Srinivas Shakkottai:

Transformers are Provably Optimal In-context Estimators for Wireless Communications. 1531-1539 - Or Raveh, Junya Honda, Masashi Sugiyama:

Multi-Player Approaches for Dueling Bandits. 1540-1548 - Hao Yan, Keith Levin:

Improved dependence on coherence in eigenvector and eigenvalue estimation error bounds. 1549-1557 - Xiangyu Chang, Yingcong Li, Muti Kara, Samet Oymak, Amit Roy-Chowdhury:

Provable Benefits of Task-Specific Prompts for In-context Learning. 1558-1566 - Chengzhi Shi, Gözde Özcan, Miquel Sirera Perelló, Yuanyuan Li, Nina Iftikhar Shamsi, Stratis Ioannidis:

Neural Point Processes for Pixel-wise Regression. 1567-1575 - Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Taishi Nishiyama, Yasuhiro Fujiwara:

Importance-weighted Positive-unlabeled Learning for Distribution Shift Adaptation. 1576-1584 - Kapilan Balagopalan, Kwang-Sung Jun:

Minimum Empirical Divergence for Sub-Gaussian Linear Bandits. 1585-1593 - Adam N. Elmachtoub, Henry Lam, Haixiang Lan, Haofeng Zhang:

Dissecting the Impact of Model Misspecification in Data-Driven Optimization. 1594-1602 - Kevin Luo, Yufan Li, Pragya Sur:

ROTI-GCV: Generalized Cross-Validation for right-ROTationally Invariant Data. 1603-1611 - Tomoya Murata, Atsushi Nitanda, Taiji Suzuki:

Clustered Invariant Risk Minimization. 1612-1620 - Gen Li, Zhihan Huang, Yuting Wei:

Towards a mathematical theory for consistency training in diffusion models. 1621-1629 - Albert Tseng, Tao Yu, Youngsuk Park:

Training LLMs with MXFP4. 1630-1638 - Mengfan Xu, Diego Klabjan:

Multi-agent Multi-armed Bandit Regret Complexity and Optimality. 1639-1647 - Chudi Zhong, Panyu Chen, Cynthia Rudin:

Models That Are Interpretable But Not Transparent. 1648-1656 - Zifan Liu, Xinran Li, Shibo Chen, Gen Li, Jiashuo Jiang, Jun Zhang:

Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control. 1657-1665 - Yiming Zhang, Parthe Pandit:

AxlePro: Momentum-Accelerated Batched Training of Kernel Machines. 1666-1674 - Xingchi Li, Guanxun Li, Xianyang Zhang:

A Likelihood Based Approach for Watermark Detection. 1675-1683 - Yufeng Zhang, Fengzhuo Zhang, Zhuoran Yang, Zhaoran Wang:

What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization. 1684-1692 - Yarden Cohen, Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman:

Bayesian Circular Regression with von Mises Quasi-Processes. 1693-1701 - Yuval Oren, Saharon Rosset:

Cross Validation for Correlated Data in Classification Models. 1702-1710 - Zhiqun Zuo, Ding Zhu, Mohammad Mahdi Khalili:

Post-processing for Fair Regression via Explainable SVD. 1711-1719 - Junyi Zhang, Angelos Dassios, Zhong Chong, Qiufei Yao:

Truncated Inverse-Lévy Measure Representation of the Beta Process. 1720-1728 - Irit Chelly, Roy Uziel, Oren Freifeld, Ari Pakman:

Consistent Amortized Clustering via Generative Flow Networks. 1729-1737 - Xinxing Shi, Thomas Baldwin-McDonald, Mauricio A. Álvarez:

Adaptive RKHS Fourier Features for Compositional Gaussian Process Models. 1738-1746 - Nguyen Thang Loi, Duong Tan Loc, Vo Nguyen Le Duy:

Statistical Inference for Feature Selection after Optimal Transport-based Domain Adaptation. 1747-1755 - Dai Hai Nguyen, Tetsuya Sakurai, Hiroshi Mamitsuka:

Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference. 1756-1764 - Juha Harviainen, Pekka Parviainen:

On Tractability of Learning Bayesian Networks with Ancestral Constraints. 1765-1773 - Aleksandar Armacki, Shuhua Yu, Pranay Sharma, Gauri Joshi, Dragana Bajovic, Dusan Jakovetic, Soummya Kar:

High-probability Convergence Bounds for Online Nonlinear Stochastic Gradient Descent under Heavy-tailed Noise. 1774-1782 - Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama:

Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation. 1783-1791 - Camille Castera, Peter Ochs:

From Learning to Optimize to Learning Optimization Algorithms. 1792-1800 - Malgorzata Lazecka, Ewa Szczurek:

Factor Analysis with Correlated Topic Model for Multi-Modal Data. 1801-1809 - Alessio Russo, Yichen Song, Aldo Pacchiano:

Pure Exploration with Feedback Graphs. 1810-1818 - Jake Fawkes, Michael O'Riordan, Athanasios Vlontzos, Oriol Corcoll, Ciarán Mark Gilligan-Lee:

The Hardness of Validating Observational Studies with Experimental Data. 1819-1827 - Konstantin Kutzkov:

Learning Graph Node Embeddings by Smooth Pair Sampling. 1828-1836 - Axel Roques, Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Laurent Oudre:

Personalized Convolutional Dictionary Learning of Physiological Time Series. 1837-1845 - Arnab Bhattacharyya, Weiming Feng, Piyush Srivastava:

Approximating the Total Variation Distance between Gaussians. 1846-1854 - António Góis, Mehrnaz Mofakhami, Fernando P. Santos, Gauthier Gidel, Simon Lacoste-Julien:

Performative Prediction on Games and Mechanism Design. 1855-1863 - Xiaoyan Hu, Ho-fung Leung, Farzan Farnia:

A Multi-Armed Bandit Approach to Online Selection and Evaluation of Generative Models. 1864-1872 - Charles Westphal, Stephen Hailes, Mirco Musolesi:

Partial Information Decomposition for Data Interpretability and Feature Selection. 1873-1881 - Yusuke Tanaka, Takaharu Yaguchi, Tomoharu Iwata, Naonori Ueda:

Energy-consistent Neural Operators for Hamiltonian and Dissipative Partial Differential Equations. 1882-1890 - Jaiden Fairoze, Guillermo Ortiz-Jiménez, Mel Vecerík, Somesh Jha, Sven Gowal:

On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark. 1891-1899 - Lucile Ter-Minassian, Liran Szlak, Ehud Karavani, Christopher C. Holmes, Yishai Shimoni:

Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation. 1900-1908 - Danial Dervovic, Michael Cashmore:

Model Evaluation in the Dark: Robust Classifier Metrics with Missing Labels. 1909-1917 - Yiming Wang, Yuxuan Song, Yiqun Wang, Minkai Xu, Rui Wang, Hao Zhou, Wei-Ying Ma:

RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation. 1918-1926 - Benjamin Howson, Sarah Filippi, Ciara Pike-Burke:

QuACK: A Multipurpose Queuing Algorithm for Cooperative k-Armed Bandits. 1927-1935 - Shirshendu Chatterjee, Soumendu Sundar Mukherjee, Tamojit Sadhukhan:

Changepoint Estimation in Sparse Dynamic Stochastic Block Models under Near-Optimal Signal Strength. 1936-1944 - Wenhao Li, Dan Qiao, Baoxiang Wang, Xiangfeng Wang, Wei Yin, Hao Shen, Bo Jin, Hongyuan Zha:

Multi-Agent Credit Assignment with Pretrained Language Models. 1945-1953 - Colin Dirren, Mattia Bianchi, Panagiotis D. Grontas, John Lygeros, Florian Dörfler:

Contractivity and linear convergence in bilinear saddle-point problems: An operator-theoretic approach. 1954-1962 - Anica Kostic, Vincent Runge, Charles Truong:

Change Point Detection in Hadamard Spaces by Alternating Minimization. 1963-1971 - Tom Kempton, Stuart Burrell, Connor Cheverall:

TempTest: Local Normalization Distortion and the Detection of Machine-generated Text. 1972-1980 - Zhongxi Fang, Xun Su, Tomohisa Tabuchi, Jianming Huang, Hiroyuki Kasai:

StableMDS: A Novel Gradient Descent-Based Method for Stabilizing and Accelerating Weighted Multidimensional Scaling. 1981-1989 - Monika Henzinger, A. R. Sricharan, Teresa Anna Steiner:

Differentially Private Continual Release of Histograms and Related Queries. 1990-1998 - Pierre Marion, Anna Korba, Peter L. Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet:

Implicit Diffusion: Efficient optimization through stochastic sampling. 1999-2007 - Sascha Xu, Sarah Mameche, Jilles Vreeken:

Information-Theoretic Causal Discovery in Topological Order. 2008-2016 - Shireen Kudukkil Manchingal, Muhammad Mubashar, Kaizheng Wang, Fabio Cuzzolin:

A Unified Evaluation Framework for Epistemic Predictions. 2017-2025 - Jie Xu, Zihan Wu, Cong Wang, Xiaohua Jia:

LMEraser: Large Model Unlearning via Adaptive Prompt Tuning. 2026-2034 - Patrik Okanovic, Andreas Kirsch, Jannes Kasper, Torsten Hoefler, Andreas Krause, Nezihe Merve Gürel:

All models are wrong, some are useful: Model Selection with Limited Labels. 2035-2043 - James Cheshire, Stéphan Clémençon:

Active Bipartite Ranking with Smooth Posterior Distributions. 2044-2052 - Francesco Bacchiocchi, Matteo Bollini, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti:

The Sample Complexity of Stackelberg Games. 2053-2061 - Marco Miani, Hrittik Roy, Søren Hauberg:

Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections. 2062-2070 - Marvin Pförtner, Jonathan Wenger, Jon Cockayne, Philipp Hennig:

Computation-Aware Kalman Filtering and Smoothing. 2071-2079 - Alex Buna, Patrick Rebeschini:

Robust Gradient Descent for Phase Retrieval. 2080-2088 - Corentin Pla, Maxime Vono, Hugo Richard:

Distribution-Aware Mean Estimation under User-level Local Differential Privacy. 2089-2097 - Disha Hegde, Mohamed Adil, Jon Cockayne:

Calibrated Computation-Aware Gaussian Processes. 2098-2106 - Zichong Wang, Nhat Hoang, Xingyu Zhang, Kevin Bello, Xiangliang Zhang, Sundararaja Sitharama Iyengar, Wenbin Zhang:

Towards Fair Graph Learning without Demographic Information. 2107-2115 - Ambrus Tamás, Szabolcs Szentpéteri, Balázs Csanád Csáji:

Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits. 2116-2124 - Zhuorui Ye, Farzan Farnia:

Gaussian Smoothing in Saliency Maps: The Stability-Fidelity Trade-Off in Neural Network Interpretability. 2125-2133 - Gunnar König, Eric Günther, Ulrike von Luxburg:

Disentangling Interactions and Dependencies in Feature Attributions. 2134-2142 - Filippo Palomba, Andrea Pugnana, José M. Álvarez, Salvatore Ruggieri:

A Causal Framework for Evaluating Deferring Systems. 2143-2151 - Sagnik Chatterjee, Manuj Mukherjee, Alhad Sethi:

Generalization Bounds for Dependent Data using Online-to-Batch Conversion. 2152-2160 - Frederiek Wesel, Kim Batselier:

Tensor Network-Constrained Kernel Machines as Gaussian Processes. 2161-2169 - Amartya Sanyal, Yaxi Hu, Yaodong Yu, Yian Ma, Yixin Wang, Bernhard Schölkopf:

Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation. 2170-2178 - Sarbojit Roy, Malik Shahid Sultan, Tania Reyes Vallejo, Leena Ali Ibrahim, Hernando Ombao:

Classification of High-dimensional Time Series in Spectral Domain Using Explainable Features with Applications to Neuroimaging Data. 2179-2187 - Alexander Koebler, Thomas Decker, Ingo Thon, Volker Tresp, Florian Buettner:

Incremental Uncertainty-aware Performance Monitoring with Active Labeling Intervention. 2188-2196 - Arthur Pignet, Chiara Regniez, John Klein:

Legitimate ground-truth-free metrics for deep uncertainty classification scoring. 2197-2205 - Zhaolu Liu, Mauricio Barahona, Robert L. Peach:

Information-Theoretic Measures on Lattices for Higher-Order Interactions. 2206-2214 - Lemin Kong, Xiangkun Hu, Tong He, David Wipf:

Common Learning Constraints Alter Interpretations of Direct Preference Optimization. 2215-2223 - Yatin Dandi, Luca Pesce, Hugo Cui, Florent Krzakala, Yue M. Lu, Bruno Loureiro:

A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities. 2224-2232 - Riccardo Poiani, Marc Jourdan, Emilie Kaufmann, Rémy Degenne:

Best-Arm Identification in Unimodal Bandits. 2233-2241 - Sebastian Pineda Arango, Pedro Mercado, Shubham Kapoor, Abdul Fatir Ansari, Lorenzo Stella, Huibin Shen, Hugo Senetaire, Ali Caner Türkmen, Oleksandr Shchur, Danielle C. Maddix, Michael Bohlke-Schneider, Bernie Wang, Syama Sundar Rangapuram:

ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables. 2242-2250 - Brian M. Cho, Dominik Meier, Kyra Gan, Nathan Kallus:

Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits. 2251-2259 - Amartya Banerjee, Harlin Lee, Nir Sharon, Caroline Moosmüller:

Efficient Trajectory Inference in Wasserstein Space Using Consecutive Averaging. 2260-2268 - Gianluca Drappo, Arnaud Robert, Marcello Restelli, Aldo A. Faisal, Alberto Maria Metelli, Ciara Pike-Burke:

Efficient Exploitation of Hierarchical Structure in Sparse Reward Reinforcement Learning. 2269-2277 - Saba Ahmadi, Siddharth Bhandari, Avrim Blum, Chen Dan, Prabhav Jain:

Distributional Adversarial Loss. 2278-2286 - Matthijs Ebbens, Nicole Funk, Jan Höckendorff, Christian Sohler, Vera Weil:

A Subquadratic Time Approximation Algorithm for Individually Fair k-Center. 2287-2295 - Yilin Xie, Shiqiang Zhang, Joel A. Paulson, Calvin Tsay:

Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation. 2296-2304 - Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Kristian Kersting, Sriraam Natarajan:

Credibility-Aware Multimodal Fusion Using Probabilistic Circuits. 2305-2313 - Pawel Teisseyre, Timo Martens, Jessa Bekker, Jesse Davis:

Learning from biased positive-unlabeled data via threshold calibration. 2314-2322 - Yihan Zhou, Eric Price, Trung Nguyen:

Near-Polynomially Competitive Active Logistic Regression. 2323-2331 - Xiaoxue Han, Huzefa Rangwala, Yue Ning:

DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification. 2332-2340 - Xiaohui Tu, Yossiri Adulyasak, Nima Akbarzadeh, Erick Delage:

Fair Resource Allocation in Weakly Coupled Markov Decision Processes. 2341-2349 - Sharmila Duppala, Juan Luque, John P. Dickerson, Seyed A. Esmaeili:

Robust Fair Clustering with Group Membership Uncertainty Sets. 2350-2358 - Saptarshi Roy, Sunrit Chakraborty, Debabrota Basu:

FLIPHAT: Joint Differential Privacy for High Dimensional Linear Bandits. 2359-2367 - Ben Aoki-Sherwood, Catherine Bregou, David Liben-Nowell, Kiran Tomlinson, Thomas Zeng:

When the Universe is Too Big: Bounding Consideration Probabilities for Plackett-Luce Rankings. 2368-2376 - Tianyu Chen, Vansh Bansal, James G. Scott:

Conditional diffusions for amortized neural posterior estimation. 2377-2385 - Tathagata Sadhukhan, Manit Paul, Raaz Dwivedi:

On adaptivity and minimax optimality of two-sided nearest neighbors. 2386-2394 - Alexandre Perez-Lebel, Gaël Varoquaux, Sanmi Koyejo, Matthieu Doutreligne, Marine Le Morvan:

Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-Calibration. 2395-2403 - Saptarshi Chakraborty, Peter L. Bartlett:

Statistical Guarantees for Unpaired Image-to-Image Cross-Domain Analysis using GANs. 2404-2412 - Florian Hübler, Ilyas Fatkhullin, Niao He:

From Gradient Clipping to Normalization for Heavy Tailed SGD. 2413-2421 - Paula Cordero-Encinar, Tobias Schröder, Peter Yatsyshin, Andrew B. Duncan:

Deep Optimal Sensor Placement for Black Box Stochastic Simulations. 2422-2430 - Sreejeet Maity, Aritra Mitra:

Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits. 2431-2439 - Chase Walker, Md Rubel Ahmed, Sumit Kumar Jha, Rickard Ewetz:

Explaining ViTs Using Information Flow. 2440-2448 - Abdullah Alchihabi, Hanping Zhang, Yuhong Guo:

Zero-Shot Action Generalization with Limited Observations. 2449-2457 - Yujia Wu, Bo Yang, Elynn Y. Chen, Yuzhou Chen, Zheshi Zheng:

Conditional Prediction ROC Bands for Graph Classification. 2458-2466 - Emanuele Troiani, Yatin Dandi, Leonardo Defilippis, Lenka Zdeborová, Bruno Loureiro, Florent Krzakala:

Fundamental computational limits of weak learnability in high-dimensional multi-index models. 2467-2475 - Arsenii Mustafin, Aleksei Pakharev, Alex Olshevsky, Ioannis Paschalidis:

MDP Geometry, Normalization and Reward Balancing Solvers. 2476-2484 - Jordan Penn, Lee M. Gunderson, Gecia Bravo Hermsdorff, Ricardo Silva, David S. Watson:

BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments. 2485-2493 - James McInerney, Nathan Kallus:

Variation Due to Regularization Tractably Recovers Bayesian Deep Learning Uncertainty. 2494-2502 - Zhiyong Wang, Jize Xie, Yi Chen, John C. S. Lui, Dongruo Zhou:

Variance-Dependent Regret Bounds for Nonstationary Linear Bandits. 2503-2511 - Michael Hellstern, Byol Kim, Zaïd Harchaoui, Ali Shojaie:

Spectral Differential Network Analysis for High-Dimensional Time Series. 2512-2520 - Steffen Schneider, Rodrigo González Laiz, Anastasiia Filippova, Markus Frey, Mackenzie W. Mathis:

Time-series attribution maps with regularized contrastive learning. 2521-2529 - Kasimir Tanner, Matteo Vilucchio, Bruno Loureiro, Florent Krzakala:

A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs. 2530-2538 - Ruichen Luo, Sebastian U. Stich, Samuel Horváth, Martin Takác:

Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis. 2539-2547 - Yeqi Gao, Zhao Song, Junze Yin:

An Iterative Algorithm for Rescaled Hyperbolic Functions Regression. 2548-2556 - Keyao Zhan, Puheng Li, Lei Wu:

Analyzing the Role of Permutation Invariance in Linear Mode Connectivity. 2557-2565 - Honghua Zhang, Benjie Wang, Marcelo Arenas, Guy Van den Broeck:

Restructuring Tractable Probabilistic Circuits. 2566-2574 - Firooz Shahriari-Mehr, Ashkan Panahi:

Asynchronous Decentralized Optimization with Constraints: Achievable Speeds of Convergence for Directed Graphs. 2575-2583 - Brendan Mallery, James M. Murphy, Shuchin Aeron:

Synthesis and Analysis of Data as Probability Measures With Entropy-Regularized Optimal Transport. 2584-2592 - Gilad Turok, Chirag Modi, Bob Carpenter:

Sampling From Multiscale Densities With Delayed Rejection Generalized Hamiltonian Monte Carlo. 2593-2601 - Ferdinando Fioretto, Diptangshu Sen, Juba Ziani:

Differentially Private Graph Data Release: Inefficiencies & Unfairness. 2602-2610 - Pedro Seber, Richard D. Braatz:

Improving N-Glycosylation and Biopharmaceutical Production Predictions Using AutoML-Built Residual Hybrid Models. 2611-2619 - Haoming Yang, Ali Hasan, Vahid Tarokh:

Parabolic Continual Learning. 2620-2628 - Antonin Schrab, Ilmun Kim:

Robust Kernel Hypothesis Testing under Data Corruption. 2629-2637 - Liran Nochumsohn, Hedi Zisling, Omri Azencot:

A Multi-Task Learning Approach to Linear Multivariate Forecasting. 2638-2646 - Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song, Yufa Zhou:

Looped ReLU MLPs May Be All You Need as Practical Programmable Computers. 2647-2655 - Daniel Marks, Dario Paccagnan:

Pick-to-Learn and Self-Certified Gaussian Process Approximations. 2656-2664 - Jia Lin Hau, Erick Delage, Esther Derman, Mohammad Ghavamzadeh, Marek Petrik:

Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis. 2665-2673 - Margarida M. Campos, João Cálem, Sophia Sklaviadis, Mário A. T. Figueiredo, André F. T. Martins:

Sparse Activations as Conformal Predictors. 2674-2682 - Fengxue Zhang, Thomas Desautels, Yuxin Chen:

Robust Multi-fidelity Bayesian Optimization with Deep Kernel and Partition. 2683-2691 - Argyrios Gerogiannis, Yu-Han Huang, Venugopal V. Veeravalli:

Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible? 2692-2700 - Andi Zhang, Tim Z. Xiao, Weiyang Liu, Robert Bamler, Damon Wischik:

Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector. 2701-2709 - Chenyang Li, Yingyu Liang, Zhenmei Shi, Zhao Song:

When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time? 2710-2718 - Haotian Sun, Antoine Moulin, Tongzheng Ren, Arthur Gretton, Bo Dai:

Spectral Representation for Causal Estimation with Hidden Confounders. 2719-2727 - Hisham Husain, Julien Monteil:

Geometric Collaborative Filtering with Convergence. 2728-2736 - Woojin Chae, Kihyuk Hong, Yufan Zhang, Ambuj Tewari, Dabeen Lee:

Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span. 2737-2745 - Tim Weiland, Marvin Pförtner, Philipp Hennig:

Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields. 2746-2754 - Michael Ito, Jiong Zhu, Dexiong Chen, Danai Koutra, Jenna Wiens:

Learning Laplacian Positional Encodings for Heterophilous Graphs. 2755-2763 - Michael Ito, Danai Koutra, Jenna Wiens:

Understanding GNNs and Homophily in Dynamic Node Classification. 2764-2772 - Alex Chen, Qing Zhou:

Causal Discovery on Dependent Binary Data. 2773-2781 - Krishna Chaitanya Kalagarla, Rahul Jain, Pierluigi Nuzzo:

A Safe Bayesian Learning Algorithm for Constrained MDPs with Bounded Constraint Violation. 2782-2790 - Shengbo Wang, Nian Si, Jose H. Blanchet, Zhengyuan Zhou:

Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces. 2791-2799 - Han Bao, Nontawat Charoenphakdee:

Calm Composite Losses: Being Improper Yet Proper Composite. 2800-2808 - Spencer Hutchinson, Tianyi Chen, Mahnoosh Alizadeh:

Optimistic Safety for Online Convex Optimization with Unknown Linear Constraints. 2809-2817 - Ruiyang Jin, Zaiwei Chen, Yiheng Lin, Jie Song, Adam Wierman:

Approximate Global Convergence of Independent Learning in Multi-Agent Systems. 2818-2826 - Hao Zhu, Daniel M. Steinberg, Piotr Koniusz:

Protein Fitness Landscape: Spectral Graph Theory Perspective. 2827-2835 - François Bachoc, Tommaso Cesari, Roberto Colomboni:

A Tight Regret Analysis of Non-Parametric Repeated Contextual Brokerage. 2836-2844 - Linlin Yu, Kangshuo Li, Pritom Kumar Saha, Yifei Lou, Feng Chen:

Evidential Uncertainty Probes for Graph Neural Networks. 2845-2853 - Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki:

Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons. 2854-2862 - Son Nguyen, Lizhang Chen, Bo Liu, Qiang Liu:

Memory-Efficient Optimization with Factorized Hamiltonian Descent. 2863-2871 - Chen Xu, Xiuyuan Cheng, Yao Xie:

Computing high-dimensional optimal transport by flow neural networks. 2872-2880 - Son Luu, Zuheng Xu, Nikola Surjanovic, Miguel Biron-Lattes, Trevor Campbell, Alexandre Bouchard-Côté:

Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs? 2881-2889 - Xin Liu:

On Preference-based Stochastic Linear Contextual Bandits with Knapsacks. 2890-2898 - Nong Minh Hieu, Jeremie Houssineau, Neil K. Chada, Emmanuel Delande:

Decoupling epistemic and aleatoric uncertainties with possibility theory. 2899-2907 - Wee Chaimanowong, Ying Zhu:

Permutation Invariant Functions: Statistical Testing, Density Estimation, and Metric Entropy. 2908-2916 - Aoran Zhang, Wenbin Zhou, Liyan Xie, Shixiang Zhu:

Recurrent Neural Goodness-of-Fit Test for Time Series. 2917-2925 - Mingliang Ma, Abolfazl Safikhani:

Transfer Learning for High-dimensional Reduced Rank Time Series Models. 2926-2934 - Abhishek Sharma, Leo Benac, Sonali Parbhoo, Finale Doshi-Velez:

Decision-Point Guided Safe Policy Improvement. 2935-2943 - Ruijia Zhang, Siliang Zeng, Chenliang Li, Alfredo García, Mingyi Hong:

Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimality. 2944-2952 - Henry Shaowu Yuchi, Shixiang Zhu, Li Dong, Yigit M. Arisoy, Matthew C. Spencer:

New User Event Prediction Through the Lens of Causal Inference. 2953-2961 - Alex Chen, Philippe Chlenski, Kenneth Munyuza, Antonio Khalil Moretti, Christian A. Naesseth, Itsik Pe'er:

Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space. 2962-2970 - Xutong Zhao, Yaqi Xie:

Multi-level Advantage Credit Assignment for Cooperative Multi-Agent Reinforcement Learning. 2971-2979 - Anand Jerry George, Nicolas Macris:

Sampling in High-Dimensions using Stochastic Interpolants and Forward-Backward Stochastic Differential Equations. 2980-2988 - Kihyuk Hong, Woojin Chae, Yufan Zhang, Dabeen Lee, Ambuj Tewari:

Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPs. 2989-2997 - Mengjing Wu, Junyu Xuan, Jie Lu:

Functional Stochastic Gradient MCMC for Bayesian Neural Networks. 2998-3006 - Tomoharu Iwata, Atsutoshi Kumagai:

Meta-learning from Heterogeneous Tensors for Few-shot Tensor Completion. 3007-3015 - Junyu Cao, Ruijiang Gao, Esmaeil Keyvanshokooh:

HR-Bandit: Human-AI Collaborated Linear Recourse Bandit. 3016-3024 - Kaan Ozkara, Bruce Huang, Ruida Zhou, Suhas N. Diggavi:

ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning. 3025-3033 - Okan Koc, Alexander Soen, Chao-Kai Chiang, Masashi Sugiyama:

Domain Adaptation and Entanglement: an Optimal Transport Perspective. 3034-3042 - Da Long, Zhitong Xu, Qiwei Yuan, Yin Yang, Shandian Zhe:

Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems. 3043-3051 - Tomoharu Iwata, Atsutoshi Kumagai, Yasutoshi Ida:

Meta-learning Task-specific Regularization Weights for Few-shot Linear Regression. 3052-3060 - Sangil Han, Kyoowon Kim, Sungkyu Jung:

Subspace Recovery in Winsorized PCA: Insights into Accuracy and Robustness. 3061-3069 - Yasunari Hikima, Ken Kobayashi, Akinori Tanaka, Akiyoshi Sannai, Naoki Hamada:

Stochastic Gradient Descent for Bézier Simplex Representation of Pareto Set in Multi-Objective Optimization. 3070-3078 - Xin Liu, Weijia Zhang, Min-Ling Zhang:

HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks. 3079-3087 - Ruicong Yao, Tim Verdonck, Jakob Raymaekers:

Causal discovery in mixed additive noise models. 3088-3096 - Leonel Rozo, Miguel González Duque, Noémie Jaquier, Søren Hauberg:

Riemann2: Learning Riemannian Submanifolds from Riemannian Data. 3097-3105 - Vu Viet Hoang, Hung The Tran, Sunil Gupta, Vu Nguyen:

High Dimensional Bayesian Optimization using Lasso Variable Selection. 3106-3114 - Jeroen Berrevoets, Jakob Raymaekers, Mihaela van der Schaar, Tim Verdonck, Ruicong Yao:

Differentiable Causal Structure Learning with Identifiability by NOTIME. 3115-3123 - Homer Durand, Gherardo Varando, Nathan Mankovich, Gustau Camps-Valls:

Out-of-distribution robustness for multivariate analysis via causal regularisation. 3124-3132 - Léo Dana, Muni Sreenivas Pydi, Yann Chevaleyre:

Memorization in Attention-only Transformers. 3133-3141 - Grigor Bezirganyan, Sana Sellami, Laure Berti-Équille, Sébastien Fournier:

Multimodal Learning with Uncertainty Quantification based on Discounted Belief Fusion. 3142-3150 - Petar Bevanda, Max Beier, Alexandre Capone, Stefan Sosnowski, Sandra Hirche, Armin Lederer:

Koopman-Equivariant Gaussian Processes. 3151-3159 - Lyuzhou Chen, Taiyu Ban, Derui Lyu, Yijia Sun, Kangtao Hu, Xiangyu Wang, Huanhuan Chen:

Continuous Structure Constraint Integration for Robust Causal Discovery. 3160-3168 - Maximilian Fleissner, Gautham Govind Anil, Debarghya Ghoshdastidar:

Infinite Width Limits of Self Supervised Neural Networks. 3169-3177 - Bassel Hamoud, Ilnura Usmanova, Kfir Yehuda Levy:

Safety in the Face of Adversity: Achieving Zero Constraint Violation in Online Learning with Slowly Changing Constraints. 3178-3186 - Rafal Karczewski, Samuel Kaski, Markus Heinonen, Vikas K. Garg:

What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much? 3187-3195 - Stefan Wahl, Armand Rousselot, Felix Draxler, Ullrich Köthe:

TRADE: Transfer of Distributions between External Conditions with Normalizing Flows. 3196-3204 - Slavomír Hanzely:

Sketch-and-Project Meets Newton Method: Global O(1/k2) Convergence with Low-Rank Updates. 3205-3213 - Tatsuya Matsukawa, Tomohiro Shiraishi, Shuichi Nishino, Teruyuki Katsuoka, Ichiro Takeuchi:

Statistical Test for Auto Feature Engineering by Selective Inference. 3214-3222 - Yue Huang, Jiaojiao Zhang, Qing Ling:

Differential Privacy in Distributed Learning: Beyond Uniformly Bounded Stochastic Gradients. 3223-3231 - Debmalya Mandal, Goran Radanovic:

Performative Reinforcement Learning with Linear Markov Decision Process. 3232-3240 - Xiusi Li, Sékou-Oumar Kaba, Siamak Ravanbakhsh:

On the Identifiability of Causal Abstractions. 3241-3249 - Ulysse Gazin, Ruth Heller, Étienne Roquain, Aldo Solari:

Powerful batch conformal prediction for classification. 3250-3258 - James Thornton, Louis Béthune, Ruixiang Zhang, Arwen Bradley, Preetum Nakkiran, Shuangfei Zhai:

Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo. 3259-3267 - Joseph Lazzaro, Ciara Pike-Burke:

Fixed-Budget Change Point Identification in Piecewise Constant Bandits. 3268-3276 - Albert Saiapin, Kim Batselier:

Tensor Network Based Feature Learning Model. 3277-3285 - Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain:

Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses. 3286-3294 - Damin Kühn, Michael T. Schaub:

Global Ground Metric Learning with Applications to scRNA data. 3295-3303 - Rilind Sahitaj, Paulius Sasnauskas, Yigit Yalin, Debmalya Mandal, Goran Radanovic:

Independent Learning in Performative Markov Potential Games. 3304-3312 - Sandeep Nagar, Girish Varma:

Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing Flows. 3313-3321 - Wenjing Han, Yueming Wu, Xinwei Sun, Lingjing Hu, Yizhou Wang:

A Differential Inclusion Approach for Learning Heterogeneous Sparsity in Neuroimaging Analysis. 3322-3330 - Daniil Tiapkin, Evgenii Chzhen, Gilles Stoltz:

Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization. 3331-3339 - Mátyás Schubert, Tom Claassen, Sara Magliacane:

SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph. 3340-3348 - Haoyang Hong, Ioanna Papanikolaou, Sonali Parbhoo:

Do Regularization Methods for Shortcut Mitigation Work As Intended? 3349-3357 - Samuele Fonio, Roberto Esposito, Marco Aldinucci:

Hyperbolic Prototypical Entailment Cones for Image Classification. 3358-3366 - Anshul Thakur, Elena Gal, Soheila Molaei, Xiao Gu, Patrick Schwab, Danielle Belgrave, Kim Branson, David A. Clifton:

Information Transfer Across Clinical Tasks via Adaptive Parameter Optimisation. 3367-3375 - Baptiste Abélès, Gergely Neu, Eugenio Clerico:

Online-to-PAC generalization bounds under graph-mixing dependencies. 3376-3384 - Wenfu Xia, Fengpei Li, Ying Sun, Ziping Zhao:

Covariance Selection over Networks. 3385-3393 - Shimeng Huang, Niklas Pfister, Jack Bowden:

Sparse Causal Effect Estimation using Two-Sample Summary Statistics in the Presence of Unmeasured Confounding. 3394-3402 - Felix Jimenez, Matthias Katzfuss:

Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks. 3403-3411 - Jonas Wahl, Jakob Runge:

Separation-Based Distance Measures for Causal Graphs. 3412-3420 - Swetha Ganesh, Washim Uddin Mondal, Vaneet Aggarwal:

Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPs. 3421-3429 - Ziqi Liu:

FreqMoE: Enhancing Time Series Forecasting through Frequency Decomposition Mixture of Experts. 3430-3438 - Feihu Huang, Chunyu Xuan, Xinrui Wang, Siqi Zhang, Songcan Chen:

Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization. 3439-3447 - Rémi Khellaf, Aurélien Bellet, Julie Josse:

Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis. 3448-3456 - Krzysztof Marcin Choromanski, Isaac Reid, Arijit Sehanobish, Kumar Avinava Dubey:

Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs. 3457-3465 - Charles Margossian, Lawrence K. Saul:

Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix. 3466-3474 - Yingqian Cui, Pengfei He, Xianfeng Tang, Qi He, Chen Luo, Jiliang Tang, Yue Xing:

A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration. 3475-3483 - Danial Davarnia, Mohammadreza Kiaghadi:

A graphical global optimization framework for parameter estimation of statistical models with nonconvex regularization functions. 3484-3492 - Daniel J. Williams, Leyang Wang, Qizhen Ying, Song Liu, Mladen Kolar:

High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching. 3493-3501 - Yixin Yan, Qiao Yang, Ziping Zhao:

Large Covariance Matrix Estimation With Nonnegative Correlations. 3502-3510 - Elisabeth Griesbauer, Claudia Czado, Arnoldo Frigessi, Ingrid Hobæk Haff:

TVineSynth: A Truncated C-Vine Copula Generator of Synthetic Tabular Data to Balance Privacy and Utility. 3511-3519 - Kaiqi Jiang, Wenzhe Fan, Mao Li, Xinhua Zhang:

Fairness Risks for Group-Conditionally Missing Demographics. 3520-3528 - Michael Lindon, Nathan Kallus:

Anytime-Valid A/B Testing of Counting Processes. 3529-3537 - Haoye Lu, Spencer Szabados, Yaoliang Yu:

Diffusion Models under Group Transformations. 3538-3546 - Sobihan Surendran, Antoine Godichon-Baggioni, Sylvain Le Corff:

Theoretical Convergence Guarantees for Variational Autoencoders. 3547-3555 - Gefan Yang, Elizabeth Louise Baker, Michael L. Severinsen, Christy Anna Hipsley, Stefan Sommer:

Infinite-dimensional Diffusion Bridge Simulation via Operator Learning. 3556-3564 - Flavio Giorgi, Cesare Campagnano, Fabrizio Silvestri, Gabriele Tolomei:

Natural Language Counterfactual Explanations for Graphs Using Large Language Models. 3565-3573 - Anna Bonnet, Maxime Sangnier:

Nonparametric estimation of Hawkes processes with RKHSs. 3574-3582 - Yuliang Ji, Jian Wu, Yuanzhe Xi:

Rethinking Neural-based Matrix Inversion: Why can't, and Where can. 3583-3591 - Tingting Ni, Maryam Kamgarpour:

A Safe Exploration Approach to Constrained Markov Decision Processes. 3592-3600 - Yang Li, Junier Oliva:

Towards Cost Sensitive Decision Making. 3601-3609 - James Odgers, Ruby Sedgwick, Chrysoula Kappatou, Ruth Misener, Sarah Filippi:

Weighted Sum of Gaussian Process Latent Variable Models. 3610-3618 - Julie Alberge, Vincent Maladière, Olivier Grisel, Judith Abécassis, Gaël Varoquaux:

Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks. 3619-3627 - Rahil Morjaria, Saikiran Bulusu, Venkata Gandikota, Sidharth Jaggi:

Density-Dependent Group Testing. 3628-3636 - Gergely Neu, Nneka Okolo:

Offline RL via Feature-Occupancy Gradient Ascent. 3637-3645 - N. Benjamin Erichson, Soon Hoe Lim, Michael W. Mahoney:

Gated Recurrent Neural Networks with Weighted Time-Delay Feedback. 3646-3654 - Masahiro Kato, Shinji Ito:

LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits. 3655-3663 - Ankur Nath, Alan Kuhnle:

Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete Optimization. 3664-3672 - David Bosch, Ashkan Panahi:

A Novel Convex Gaussian Min Max Theorem for Repeated Features. 3673-3681 - Lucas Gnecco Heredia, Matteo Sammut, Muni Sreenivas Pydi, Rafael Pinot, Benjamin Négrevergne, Yann Chevaleyre:

Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory. 3682-3690 - Reda Marzouk, Shahaf Bassan, Guy Katz, Colin de la Higuera:

On the Computational Tractability of the (Many) Shapley Values. 3691-3699 - Ignavier Ng, Shaoan Xie, Xinshuai Dong, Peter Spirtes, Kun Zhang:

Causal Representation Learning from General Environments under Nonparametric Mixing. 3700-3708 - Adrien Corenflos, Zheng Zhao, Thomas B. Schön, Simo Särkkä, Jens Sjölund:

Conditioning diffusion models by explicit forward-backward bridging. 3709-3717 - Shaan Ul Haque, Siva Theja Maguluri:

Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem. 3718-3726 - Gabriel Moreira, Manuel Marques, João Paulo Costeira, Alexander G. Hauptmann:

Learning Visual-Semantic Subspace Representations. 3727-3735 - Liyuan Xu, Arthur Gretton:

Kernel Single Proxy Control for Deterministic Confounding. 3736-3744 - Arkapal Panda, Utpal Garain:

Copula Based Trainable Calibration Error Estimator of Multi-Label Classification with Label Interdependencies. 3745-3753 - Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, Murat Kocaoglu:

Causal Discovery-Driven Change Point Detection in Time Series. 3754-3762 - Parjanya Prajakta Prashant, Seyedeh Baharan Khatami, Bruno Ribeiro, Babak Salimi:

Scalable Out-of-Distribution Robustness in the Presence of Unobserved Confounders. 3763-3771 - Ross Irwin, Alessandro Tibo, Jon Paul Janet, Simon Olsson:

SemlaFlow - Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching. 3772-3780 - Jeongyeol Kwon, Luke Dotson, Yudong Chen, Qiaomin Xie:

Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way. 3781-3789 - David Huk, Mark Steel, Ritabrata Dutta:

Your copula is a classifier in disguise: classification-based copula density estimation. 3790-3798 - Ziyu Wang, Christopher C. Holmes:

On Subjective Uncertainty Quantification and Calibration in Natural Language Generation. 3799-3807 - Yang Hu, Tianyi Chen, Na Li, Kai Wang, Bo Dai:

Primal-Dual Spectral Representation for Off-policy Evaluation. 3808-3816 - Yunyi Shen, Renato Berlinghieri, Tamara Broderick:

Multi-marginal Schrödinger Bridges with Iterative Reference Refinement. 3817-3825 - Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, Serguei Barannikov:

RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks. 3826-3834 - Yuyang Deng, Fuli Qiao, Mehrdad Mahdavi:

Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees. 3835-3843 - Hamish Flynn, David Reeb:

Tighter Confidence Bounds for Sequential Kernel Regression. 3844-3852 - Dheeraj Baby, Boran Han, Shuai Zhang, Cuixiong Hu, Bernie Wang, Yuxiang Wang:

Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach. 3853-3861 - Didier Chételat, Joseph Cotnareanu, Rylee Thompson, Yingxue Zhang, Mark Coates:

InnerThoughts: Disentangling Representations and Predictions in Large Language Models. 3862-3870 - Meltem Tatli, Arpan Mukherjee, Prashanth L. A., Karthikeyan Shanmugam, Ali Tajer:

Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms. 3871-3879 - Ji Won Park, Kyunghyun Cho:

Semiparametric conformal prediction. 3880-3888 - Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche:

Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networks. 3889-3897 - Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Christian A. Naesseth, Eric T. Nalisnick:

Max-Rank: Efficient Multiple Testing for Conformal Prediction. 3898-3906 - Zilong Deng, Simon Khan, Shaofeng Zou:

Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model. 3907-3915 - Shyam Venkatasubramanian, Ali Pezeshki, Vahid Tarokh:

Steinmetz Neural Networks for Complex-Valued Data. 3916-3924 - Juan Ramirez, Ignacio Hounie, Juan Elenter, Jose Gallego-Posada, Meraj Hashemizadeh, Alejandro Ribeiro, Simon Lacoste-Julien:

Feasible Learning. 3925-3933 - Tyler LaBonte, Kuo-Wei Lai, Vidya Muthukumar:

Task Shift: From Classification to Regression in Overparameterized Linear Models. 3934-3942 - Reza Asad, Reza Babanezhad Harikandeh, Issam H. Laradji, Nicolas Le Roux, Sharan Vaswani:

Fast Convergence of Softmax Policy Mirror Ascent. 3943-3951 - Ali Azizpour, Nicolas Zilberstein, Santiago Segarra:

Scalable Implicit Graphon Learning. 3952-3960 - Cheng Jiang, Sitian Qian:

Application of Structured State Space Models to High energy physics with locality sensitive hashing. 3961-3969 - Mayleen Cortez-Rodriguez, Matthew Eichhorn, Christina Lee Yu:

Analysis of Two-Stage Rollout Designs with Clustering for Causal Inference under Network Interference. 3970-3978 - Gintare Karolina Dziugaite, Daniel M. Roy:

The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws. 3979-3987 - Jonathan Geuter, Clément Bonet, Anna Korba, David Alvarez-Melis:

DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows. 3988-3996 - Blaise Delattre, Paul Caillon, Quentin Barthélemy, Erwan Fagnou, Alexandre Allauzen:

Bridging the Theoretical Gap in Randomized Smoothing. 3997-4005 - Sungee Hong, Zhengling Qi, Raymond K. W. Wong:

Distributional Off-policy Evaluation with Bellman Residual Minimization. 4006-4014 - Christina Baek, Aditi Raghunathan, J. Zico Kolter:

Theory of Agreement-on-the-Line in Linear Models and Gaussian Data. 4015-4023 - Peihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang, Sreyas Mohan, Forrest N. Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra:

SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity. 4024-4032 - Haolin Zou, Arnab Auddy, Kamiar Rahnama Rad, Arian Maleki:

Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings. 4033-4041 - Han Cui, Zhiyuan Yu, Jingbo Liu:

Sampling from the Random Linear Model via Stochastic Localization Up to the AMP Threshold. 4042-4050 - Ashwin Renganathan, Kade Carlson:

qttPOTS: Efficient Batch Multiobjective Bayesian Optimization via Pareto Optimal Thompson Sampling. 4051-4059 - Chanwoo Chun, SueYeon Chung, Daniel D. Lee:

Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices. 4060-4068 - Maryam Aliakbarpour, Syomantak Chaudhuri, Thomas A. Courtade, Alireza Fallah, Michael I. Jordan:

Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy. 4069-4077 - Mingyu Kim, Jongwoo Ko, Mijung Park:

Bayesian Principles Improve Prompt Learning In Vision-Language Models. 4078-4086 - Enea Monzio Compagnoni, Rustem Islamov, Frank Norbert Proske, Aurélien Lucchi:

Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs. 4087-4095 - Maryam Aliakbarpour, Konstantina Bairaktari, Adam Smith, Marika Swanberg, Jonathan R. Ullman:

Privacy in Metalearning and Multitask Learning: Modeling and Separations. 4096-4104 - Paulius Rauba, Qiyao Wei, Mihaela van der Schaar:

Visualizing token importance for black-box language models. 4105-4113 - Teodor Rotaru, Panagiotis Patrinos, François Glineur:

Tight Analysis of Difference-of-Convex Algorithm (DCA) Improves Convergence Rates for Proximal Gradient Descent. 4114-4122 - Emanuele Marconato, Sébastien Lachapelle, Sebastian Weichwald, Luigi Gresele:

All or None: Identifiable Linear Properties of Next-Token Predictors in Language Modeling. 4123-4131 - Arnab Bhattacharyya, Constantinos Daskalakis, Themis Gouleakis, Yuhao Wang:

Learning High-dimensional Gaussians from Censored Data. 4132-4140 - Jungeum Kim, Percy S. Zhai, Veronika Rocková:

Deep Generative Quantile Bayes. 4141-4149 - Houssam Zenati, Judith Abécassis, Julie Josse, Bertrand Thirion:

Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments. 4150-4158 - Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann, Wieland Brendel:

InfoNCE: Identifying the Gap Between Theory and Practice. 4159-4167 - Alessio Russo, Alberto Maria Metelli, Marcello Restelli:

Achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ Regret in Average-Reward POMDPs with Known Observation Models. 4168-4176 - Jung Yeon Park, Sujay Bhatt, Sihan Zeng, Lawson L. S. Wong, Alec Koppel, Sumitra Ganesh, Robin Walters:

Approximate Equivariance in Reinforcement Learning. 4177-4185 - Yuying Duan, Gelei Xu, Yiyu Shi, Michael Lemmon:

The cost of local and global fairness in Federated Learning. 4186-4194 - Nishant Panda, Jehanzeb H. Chaudhry, Natalie Klein, James Carzon, Troy D. Butler:

Local Stochastic Sensitivity Analysis For Dynamical Systems. 4195-4203 - Berfin Simsek, Amire Bendjeddou, Daniel Hsu:

Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence. 4204-4212 - Ha Manh Bui, Enrique Mallada, Anqi Liu:

Variance-Aware Linear UCB with Deep Representation for Neural Contextual Bandits. 4213-4221 - Jakwang Kim, Jiyoung Park, Anirban Bhattacharya:

Robust Estimation in metric spaces: Achieving Exponential Concentration with a Fréchet Median. 4222-4230 - Wenyuan Zhao, Haoyuan Chen, Tie Liu, Rui Tuo, Chao Tian:

From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation. 4231-4239 - Christian Toth, Christian Knoll, Franz Pernkopf, Robert Peharz:

Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders. 4240-4248 - Wenjing Chen, Victoria G. Crawford:

Linear Submodular Maximization with Bandit Feedback. 4249-4257 - Jayoung Ryu, Charlotte Bunne, Luca Pinello, Aviv Regev, Romain Lopez:

Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport. 4258-4266 - Siddharth Vishwanath, Jonathan Hehir:

Signal Recovery from Random Dot-Product Graphs under Local Differential Privacy. 4267-4275 - Bijan Mazaheri, Chandler Squires, Caroline Uhler:

Synthetic Potential Outcomes and Causal Mixture Identifiability. 4276-4284 - Sudeep Salgia, Nikola Pavlovic, Yuejie Chi, Qing Zhao:

Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization. 4285-4293 - Juliusz Ziomek, Masaki Adachi, Michael A. Osborne:

Time-varying Gaussian Process Bandits with Unknown Prior. 4294-4302 - Ojash Neopane, Aaditya Ramdas, Aarti Singh:

Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect. 4303-4311 - Yulong Yang, Bowen Feng, Keqin Wang, Naomi Ehrich Leonard, Adji Bousso Dieng, Christine Allen-Blanchette:

Behavior-Inspired Neural Networks for Relational Inference. 4312-4320 - Antonios Valkanas, Boris N. Oreshkin, Mark Coates:

MODL: Multilearner Online Deep Learning. 4321-4329 - Julianna Piskorz, Nicolás Astorga, Jeroen Berrevoets, Mihaela van der Schaar:

Active Feature Acquisition for Personalised Treatment Assignment. 4330-4338 - Hao Liu, Junze Ye, Jose H. Blanchet, Nian Si:

ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters. 4339-4347 - Arindam Banerjee, Qiaobo Li, Yingxue Zhou:

Loss Gradient Gaussian Width based Generalization and Optimization Guarantees. 4348-4356 - Qiaobo Li, Zixiang Chen, Yihe Deng, Yiwen Kou, Yuan Cao, Quanquan Gu:

On the Power of Multitask Representation Learning with Gradient Descent. 4357-4365 - Seyedeh Baharan Khatami, Harsh Parikh, Haowei Chen, Sudeepa Roy, Babak Salimi:

Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects. 4366-4374 - Xiangyu Guo, Ricardo Henao:

Cross-Modal Imputation and Uncertainty Estimation for Spatial Transcriptomics. 4375-4383 - Sarah Alnegheimish, Zelin He, Matthew Reimherr, Akash Chandrayan, Abhinav Pradhan, Luca D'Angelo:

M2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding. 4384-4392 - Siyan Zhao, Daniel Israel, Guy Van den Broeck, Aditya Grover:

Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models. 4393-4401 - Kaan Ozkara, Tao Yu, Youngsuk Park:

Stochastic Rounding for LLM Training: Theory and Practice. 4402-4410 - Sebastian Salazar, Michal Kucer, Yixin Wang, Emily M. Casleton, David M. Blei:

Posterior Mean Matching: Generative Modeling through Online Bayesian Inference. 4411-4419 - Avinandan Bose, Simon Shaolei Du, Maryam Fazel:

Offline Multi-task Transfer RL with Representational Penalization. 4420-4428 - Debmalya Mandal, Andi Nika, Parameswaran Kamalaruban, Adish Singla, Goran Radanovic:

Corruption Robust Offline Reinforcement Learning with Human Feedback. 4429-4437 - Avinandan Bose, Laurent Lessard, Maryam Fazel, Krishnamurthy Dj Dvijotham:

Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoning. 4438-4446 - Bo Chen, Xiaoyu Li, Yingyu Liang, Zhenmei Shi, Zhao Song:

Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent. 4447-4455 - Yigit Efe Erginbas, Thomas A. Courtade, Kannan Ramchandran:

Online Assortment and Price Optimization Under Contextual Choice Models. 4456-4464 - Shivvrat Arya, Tahrima Rahman, Vibhav Gogate:

SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings. 4465-4473 - Pasan Dissanayake, Faisal Hamman, Barproda Halder, Ilia Sucholutsky, Qiuyi Zhang, Sanghamitra Dutta:

Quantifying Knowledge Distillation using Partial Information Decomposition. 4474-4482 - Haotian Ye, Himanshu Jain, Chong You, Ananda Theertha Suresh, Haowei Lin, James Zou, Felix X. Yu:

Efficient and Asymptotically Unbiased Constrained Decoding for Large Language Models. 4483-4491 - Di Wu, Chengshuai Shi, Ruida Zhou, Cong Shen:

Cost-Aware Optimal Pairwise Pure Exploration. 4492-4500 - Fanqi Yan, Huy Nguyen, Le Quang Dung, Pedram Akbarian, Nhat Ho:

Understanding Expert Structures on Minimax Parameter Estimation in Contaminated Mixture of Experts. 4501-4509 - Chirag Modi, Diana Cai, Lawrence K. Saul:

Batch, match, and patch: low-rank approximations for score-based variational inference. 4510-4518 - Moule Lin, Shuhao Guan, Weipeng Jing, Goetz Botterweck, Andrea Patane:

Stochastic Weight Sharing for Bayesian Neural Networks. 4519-4527 - Qiran Dong, Paul Grigas, Vishal Gupta:

Beyond Discretization: Learning the Optimal Solution Path. 4528-4536 - Tong Yang, Jincheng Mei, Hanjun Dai, Zixin Wen, Shicong Cen, Dale Schuurmans, Yuejie Chi, Bo Dai:

Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment. 4537-4545 - Abdellah Rahmani, Pascal Frossard:

Causal Temporal Regime Structure Learning. 4546-4554 - Matthew Werenski, Brendan Mallery, Shuchin Aeron, James M. Murphy:

Linearized Wasserstein Barycenters: Synthesis, Analysis, Representational Capacity, and Applications. 4555-4563 - Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang:

General Staircase Mechanisms for Optimal Differential Privacy. 4564-4572 - Juntong Chen, Johannes Schmidt-Hieber, Claire Donnat, Olga Klopp:

Understanding the Effect of GCN Convolutions in Regression Tasks. 4573-4581 - Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortoli, Dongxia Wu, Haorui Wang, Aaron M. Ferber, Yian Ma, Carla P. Gomes, Chao Zhang:

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints. 4582-4590 - Tobias Wegel, Filip Kovacevic, Alexandru Tifrea, Fanny Yang:

Learning Pareto manifolds in high dimensions: How can regularization help? 4591-4599 - Xinyang Liu, Hengrong Du, Wei Deng, Ruqi Zhang:

Optimal Stochastic Trace Estimation in Generative Modeling. 4600-4608 - Krzysztof Kacprzyk, Mihaela van der Schaar:

Beyond Size-Based Metrics: Measuring Task-Specific Complexity in Symbolic Regression. 4609-4617 - Nikola Pavlovic, Sudeep Salgia, Qing Zhao:

Differentially Private Kernelized Contextual Bandits. 4618-4626 - Baris Askin, Pranay Sharma, Gauri Joshi, Carlee Joe-Wong:

Federated Communication-Efficient Multi-Objective Optimization. 4627-4635 - Ziqing Xu, Hancheng Min, Lachlan Ewen MacDonald, Jinqi Luo, Salma Tarmoun, Enrique Mallada, René Vidal:

Understanding the Learning Dynamics of LoRA: A Gradient Flow Perspective on Low-Rank Adaptation in Matrix Factorization. 4636-4644 - Kevin Rojas, Yixin Tan, Molei Tao, Yuriy Nevmyvaka, Wei Deng:

Variational Schrödinger Momentum Diffusion. 4645-4653 - Csaba Tóth, Masaki Adachi, Michael A. Osborne, Harald Oberhauser:

Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes. 4654-4662 - Calvin Osborne, Eliza O'Reilly:

The Uniformly Rotated Mondrian Kernel. 4663-4671 - Fuqiang Liu, Sicong Jiang, Luis Miranda-Moreno, Seongjin Choi, Lijun Sun:

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting. 4672-4680 - Juncheng Dong, Hao-Lun Hsu, Qitong Gao, Vahid Tarokh, Miroslav Pajic:

Variational Adversarial Training Towards Policies with Improved Robustness. 4681-4689 - Yang Yang, Kai Zhang, Ping-Shou Zhong:

Testing Conditional Independence with Deep Neural Network Based Binary Expansion Testing (DeepBET). 4690-4698 - Nikola Pavlovic, Sudeep Salgia, Qing Zhao:

Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness. 4699-4707 - Mahbod Majid, Rattana Pukdee, Vishwajeet Agrawal, Burak Varici, Pradeep Kumar Ravikumar:

On the Consistent Recovery of Joint Distributions from Conditionals. 4708-4716 - Mohammadreza M. Kalan, Eitan J. Neugut, Samory Kpotufe:

Transfer Neyman-Pearson Algorithm for Outlier Detection. 4717-4725 - Ritwik Vashistha, Arya Farahi:

I-trustworthy Models. A framework for trustworthiness evaluation of probabilistic classifiers. 4726-4734 - Zhi Zhang, Kyle Ritscher, Oscar Hernan Madrid Padilla:

Quantile Additive Trend Filtering. 4735-4743 - Beomjun Kim, Jaehwan Kim, Kangyeon Kim, Sunwoo Kim, Heejin Ahn:

A Computation-Efficient Method of Measuring Dataset Quality based on the Coverage of the Dataset. 4744-4752 - Katherine Tieu, Dongqi Fu, Jun Wu, Jingrui He:

Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problem. 4753-4761 - Hongni Wang, Junxi Zhang, Na Li, Linglong Kong, Bei Jiang, Xiaodong Yan:

Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data. 4762-4770 - Zixin Kuang, Meng-Fen Chiang, Wang-Chien Lee:

A Shapley-value Guided Rationale Editor for Rationale Learning. 4771-4779 - Yan Yang, Bin Gao, Ya-xiang Yuan:

Bilevel Reinforcement Learning via the Development of Hyper-gradient without Lower-Level Convexity. 4780-4788 - Behrooz Tahmasebi, Stefanie Jegelka:

Regularity in Canonicalized Models: A Theoretical Perspective. 4789-4797 - Rina Dechter, Anna Raichev, Jin Tian, Alexander Ihler:

Graph-based Complexity for Causal Effect by Empirical Plug-in. 4798-4806 - Ricardo Baptista, Aram-Alexandre Pooladian, Michael Brennan, Youssef Marzouk, Jonathan Niles-Weed:

Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps. 4807-4815 - Ashkan Soleymani, Behrooz Tahmasebi, Stefanie Jegelka, Patrick Jaillet:

A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries. 4816-4824 - Xianwen Deng, Yijun Wang, Zhi Xue:

Leveraging Frozen Batch Normalization for Co-Training in Source-Free Domain Adaptation. 4825-4833 - Yi Fu, Anthony Tompkins, Yang Song, Maurice Pagnucco:

Structure based SAT dataset for analysing GNN generalisation. 4834-4842 - Angeliki Giannou, Liu Yang, Tianhao Wang, Dimitris Papailiopoulos, Jason D. Lee:

How Well Can Transformers Emulate In-Context Newton's Method? 4843-4851 - Cheng Peng, Stan Uryasev:

Nonparametric Distributional Regression via Quantile Regression. 4852-4860 - Naichang Ke, Ryogo Tanaka, Yoshinobu Kawahara:

Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators. 4861-4869 - Ayoub Ghriss, Claire Monteleoni:

Deep Clustering via Probabilistic Ratio-Cut Optimization. 4870-4878 - Clément Louis Canonne, Themis Gouleakis, Yuhao Wang, Joy Qiping Yang:

Gaussian Mean Testing under Truncation. 4879-4887 - Baozhen Wang, Xingye Qiao:

Conformal Prediction Under Generalized Covariate Shift with Posterior Drift. 4888-4896 - Aldo Gael Carranza, Susan Athey:

Robust Offline Policy Learning with Observational Data from Multiple Sources. 4897-4905 - Qingshi Sun, Nathan Justin, Andrés Gómez, Phebe Vayanos:

Mixed-Feature Logistic Regression Robust to Distribution Shifts. 4906-4914 - Jarren Briscoe, Garrett Kepler, Daryl Deford, Assefaw H. Gebremedhin:

Algorithmic Accountability in Small Data: Sample-Size-Induced Bias Within Classification Metrics. 4915-4923 - Viacheslav Yusupov, Maxim V. Rakhuba, Evgeny Frolov:

Knowledge Graph Completion with Mixed Geometry Tensor Factorization. 4924-4932 - Alix Lhéritier, Maurizio Filippone:

Unconditionally Calibrated Priors for Beta Mixture Density Networks. 4933-4941 - Simon Vary, David Martínez-Rubio, Patrick Rebeschini:

Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization. 4942-4950 - Mingyu Pu, Songhao Wang, Haowei Wang, Szu Hui Ng:

Weighted Euclidean Distance Matrices over Mixed Continuous and Categorical Inputs for Gaussian Process Models. 4951-4959 - Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer, Maurizio Filippone:

Robust Classification by Coupling Data Mollification with Label Smoothing. 4960-4968 - Omer Noy Klein, Alihan Hüyük, Ron Shamir, Uri Shalit, Mihaela van der Schaar:

Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions. 4969-4977 - Francesco Micheli, Efe C. Balta, Anastasios Tsiamis, John Lygeros:

Wasserstein Distributionally Robust Bayesian Optimization with Continuous Context. 4978-4986 - Talal Alrawajfeh, Joonas Jälkö, Antti Honkela:

Noise-Aware Differentially Private Variational Inference. 4987-4995 - Nicolas Menet, Jonas Hübotter, Parnian Kassraie, Andreas Krause:

LITE: Efficiently Estimating Gaussian Probability of Maximality. 4996-5004 - Vorapong Suppakitpaisarn, Donlapark Ponnoprat, Nicha Hirankarn, Quentin Hillebrand:

Counting Graphlets of Size k under Local Differential Privacy. 5005-5013 - Daniel Paulin, Peter A. Whalley, Neil K. Chada, Benedict J. Leimkuhler:

Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics. 5014-5022 - Paul Mangold, Alain Oliviero Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines:

Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation. 5023-5031 - Mikkel Jordahn, Jonas Vestergaard Jensen, Mikkel N. Schmidt, Michael Riis Andersen:

On Local Posterior Structure in Deep Ensembles. 5032-5040 - Boning Zhang, Dongzhu Liu, Osvaldo Simeone, Guanchu Wang, Dimitrios Pezaros, Guangxu Zhu:

Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning. 5041-5049 - Zhi Zhang, Chris Chow, Yasi Zhang, Yanchao Sun, Haochen Zhang, Eric Hanchen Jiang, Han Liu, Furong Huang, Yuchen Cui, Oscar Hernan Madrid Padilla:

Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory. 5050-5058 - Aaron Mishkin, Alberto Bietti, Robert M. Gower:

Level Set Teleportation: An Optimization Perspective. 5059-5067 - Satish Kumar Keshri, Nazreen Shah, Ranjitha Prasad:

On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients. 5068-5076 - Daniel Galperin, Ullrich Köthe:

Analyzing Generative Models by Manifold Entropic Metrics. 5077-5085 - Salma Kharrat, Marco Canini, Samuel Horváth:

DPFL: Decentralized Personalized Federated Learning. 5086-5094 - Bariscan Bozkurt, Ben Deaner, Dimitri Meunier, Liyuan Xu, Arthur Gretton:

Density Ratio-based Proxy Causal Learning Without Density Ratios. 5095-5103 - Hue Dang, Matthew Wicker, Goetz Botterweck, Andrea Patane:

Certifiably Quantisation-Robust training and inference of Neural Networks. 5104-5112 - Alicja Raczkowska, Aleksandra Osowska-Kurczab, Jacek Szczerbinski, Kalina Jasinska-Kobus, Klaudia Nazarko:

AlleNoise - large-scale text classification benchmark dataset with real-world label noise. 5113-5121 - Daniel Csillag, Cláudio José Struchiner, Guilherme Tegoni Goedert:

Strategic Conformal Prediction. 5122-5130 - Roman Malashin, Valeria Yachnaya, Alexandr V. Mullin:

Hypernym Bias: Unraveling Deep Classifier Training Dynamics through the Lens of Class Hierarchy. 5131-5139 - Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer, Julia Herbinger:

Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory. 5140-5148 - Xudong Sun, Nutan Chen, Alexej Gossmann, Yu Xing, Matteo Wohlrapp, Emilio Dorigatti, Carla Feistner, Felix Drost, Daniele Scarcella, Lisa Beer, Carsten Marr:

M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling. 5149-5157 - Erfan Mirzaei, Andreas Maurer, Vladimir R. Kostic, Massimiliano Pontil:

An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and Applications. 5158-5166 - Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato:

Training Neural Samplers with Reverse Diffusive KL Divergence. 5167-5175 - Fares Fourati, Salma Kharrat, Vaneet Aggarwal, Mohamed-Slim Alouini:

Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz Constants. 5176-5184 - Marcus Häggbom, Morten Karlsmark, Joakim Andén:

Mean-Field Microcanonical Gradient Descent. 5185-5193 - Daniel Guzman-Olivares, Philipp Schmidt, Jacek Golebiowski, Artur Bekasov:

Clustering Context in Off-Policy Evaluation. 5194-5202 - Long Tung Vuong:

Task-Driven Discrete Representation Learning. 5203-5211 - Amir Joudaki, Thomas Hofmann:

Emergence of Globally Attracting Fixed Points in Deep Neural Networks With Nonlinear Activations. 5212-5220 - Qizhang Feng, Siva Rajesh Kasa, Santhosh Kumar Kasa, Hyokun Yun, Choon Hui Teo, Sravan Babu Bodapati:

Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment. 5221-5229 - Bailey Andrew, David R. Westhead, Luisa Cutillo:

The Strong Product Model for Network Inference without Independence Assumptions. 5230-5238 - Uday Kiran Reddy Tadipatri, Benjamin David Haeffele, Joshua Agterberg, René Vidal:

A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks. 5239-5247

manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.


Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID














