default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID28th 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.
last updated on 2025-12-04 01:32 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
