AISTATS 2025 Accepted Papers

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A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets Ossi Räisä · Antti Honkela
Looped ReLU MLPs May Be All You Need as Practical Programmable Computers Yingyu Liang · Zhizhou Sha · Zhenmei Shi · Zhao Song · Yufa Zhou
The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws Gintare Karolina Dziugaite · Daniel Roy
Strong Screening Rules for Group-based SLOPE Models Fabio Feser · Marina Evangelou
Sketch-and-Project Meets Newton Method: Global O(1/k^2) Convergence with Low-Rank Updates Slavomir Hanzely
Ant Colony Sampling with GFlowNets for Combinatorial Optimization Minsu Kim · Sanghyeok Choi · Hyeonah Kim · Jiwoo Son · Jinkyoo Park · Yoshua Bengio
Sampling in High-Dimensions using Stochastic Interpolants and Forward-Backward Stochastic Differential Equations Anand Jerry George · Nicolas Macris
Approximate Global Convergence of Independent Learning in Multi-Agent Systems Ruiyang Jin · Zaiwei Chen · Yiheng Lin · Jie Song · Adam Wierman
Stochastic Gradient Descent for Bézier Simplex Representation of Pareto Set in Multi-Objective Optimization Yasunari Hikima · Ken Kobayashi · Akinori Tanaka · Akiyoshi Sannai · Naoki Hamada
A Safe Exploration Approach to Constrained Markov Decision Processes tingting Ni · Maryam Kamgarpour
Robust Fair Clustering with Group Membership Uncertainty Sets Sharmila Duppala · Juan Luque · John Dickerson · Seyed Esmaeili
SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching Ross Irwin · Alessandro Tibo · Jon Paul Janet · Simon Olsson
Bayesian Circular Regression with von Mises Quasi-Processes Yarden Cohen · Alexandre Wu Navarro · Jes Frellsen · Richard Turner · Raziel Riemer · Ari Pakman
Offline RL via Feature-Occupancy Gradient Ascent Gergely Neu · Nneka Okolo
Semiparametric conformal prediction Ji Won Park · Kyunghyun Cho
Learning a Single Index Model from Anisotropic Data with Vanilla Stochastic Gradient Descent Guillaume Braun · Minh Ha Quang · Masaaki Imaizumi
Choice is what matters after Attention Chenhan Fu · guoming wang · Juncheng Li · Rongxing Lu · Siliang Tang
Best-Arm Identification in Unimodal Bandits Riccardo Poiani · Marc Jourdan · Emilie Kaufmann · Rémy Degenne
FLIPHAT: Joint Differential Privacy for High Dimensional Linear Bandits Saptarshi Roy · Sunrit Chakraborty · Debabrota Basu
Near-Optimal Algorithm for Non-Stationary Kernelized Bandits Shogo Iwazaki · Shion Takeno
Locally Private Estimation with Public Features Yuheng Ma · Ke Jia · Hanfang Yang
Common Learning Constraints Alter Interpretations of Direct Preference Optimization Lemin Kong · Xiangkun Hu · Tong He · David Wipf
Statistical Guarantees for Unpaired Image-to-Image Cross-Domain Analysis using GANs Saptarshi Chakraborty · Peter Bartlett
Learning Pareto manifolds in high dimensions: How can regularization help? Tobias Wegel · Filip Kovačević · Alexandru Tifrea · Fanny Yang
SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph Mátyás Schubert · Tom Claassen · Sara Magliacane
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation Lucile Ter-Minassian · Liran Szlak · Ehud Karavani · Chris Holmes · Yishai Shimoni
Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties David Martínez-Rubio · Christophe Roux · Christopher Criscitiello · Sebastian Pokutta
Sequential Kernelized Stein Discrepancy Diego Martinez-Taboada · Aaditya Ramdas
Learning-Augmented Algorithms for Online Concave Packing and Convex Covering Problems Elena Grigorescu · Young-San Lin · Maoyuan Song
Steinmetz Neural Networks for Complex-Valued Data Shyam Venkatasubramanian · Ali Pezeshki · Vahid Tarokh
Adversarial Training in High-Dimensional Regression: Generated Data and Neural Networks Yue Xing
A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks Uday Kiran Reddy Tadipatri · Ben Haeffele · Joshua Agterberg · Rene Vidal
Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields Tim Weiland · Marvin Pförtner · Philipp Hennig
Selecting the Number of Communities for Weighted Degree-Corrected Stochastic Block Models Yucheng Liu · Xiaodong Li
Efficient and Asymptotically Unbiased Constrained Decoding for Large Language Models Haotian Ye · Himanshu Jain · Chong You · Ananda Theertha Suresh · Haowei Lin · James Zou · Felix Yu
Balls-and-Bins Sampling for DP-SGD Lynn Chua · Badih Ghazi · Charlie Harrison · Pritish Kamath · Ravi Kumar · Ethan Leeman · Pasin Manurangsi · Amer Sinha · Chiyuan Zhang
Transformers are Provably Optimal In-context Estimators for Wireless Communications Vishnu Teja Kunde · Vicram Rajagopalan · Chandra Shekhara Kaushik Valmeekam · Krishna Narayanan · Jean-Francois Chamberland · Dileep Kalathil · Srinivas Shakkottai
A Computation-Efficient Method of Measuring Dataset Quality based on the Coverage of the Dataset BEOMJUN KIM · Jaehwan Kim · Kangyeon Kim · Sunwoo Kim · Heejin Ahn
A Multi-Task Learning Approach to Linear Multivariate Forecasting Liran Nochumsohn · Hedi Zisling · Omri Azencot
S-CFE: Simple Counterfactual Explanations Shpresim Sadiku · Moritz Wagner · Sai Ganesh Nagarajan · Sebastian Pokutta
Is Merging Worth It? Securely Evaluating the Information Gain for Causal Dataset Acquisition Jake Fawkes · Lucile Ter-Minassian · Desi Ivanova · Uri Shalit · Chris Holmes
Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices Chanwoo Chun · SueYeon Chung · Daniel Lee
HAR-former: Hybrid Transformer with an Adaptive Time-Frequency Representation Matrix for Long-Term Series Forecasting kenghao zheng · ZI LONG · Shuxin Wang
Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments Houssam Zenati · Judith Abécassis · julie Josse · Bertrand Thirion
Optimal estimation of linear non-Gaussian structure equation models Sunmin Oh · Seungsu Han · Gunwoong Park
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation Koshi Watanabe · Keisuke Maeda · Takahiro Ogawa · Miki Haseyama
Performative Prediction on Games and Mechanism Design Antonio Gois · Mehrnaz Mofakhami · Fernando Santos · Simon Lacoste-Julien · Gauthier Gidel
Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons Wei Huang · Yuan Cao · Haonan Wang · Xin Cao · Taiji Suzuki
Tamed Langevin sampling under weaker conditions Iosif Lytras · Panayotis Mertikopoulos
InfoNCE: Identifying the Gap Between Theory and Practice Evgenia Rusak · Patrik Reizinger · Attila Juhos · Oliver Bringmann · Roland Zimmermann · Wieland Brendel
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning Kaan Ozkara · Bruce Huang · Ruida Zhou · Suhas Diggavi
Gated Recurrent Neural Networks with Weighted Time-Delay Feedback N. Benjamin Erichson · Soon Hoe Lim · Michael Mahoney
Bayesian Gaussian Process ODEs via Double Normalizing Flows jian xu · Shian Du · Junmei Yang · Xinghao Ding · Delu Zeng · John Paisley
Quantifying Knowledge Distillation using Partial Information Decomposition Sachindra Pasan Dissanayake · Faisal Hamman · Barproda Halder · Ilia Sucholutsky · Qiuyi Zhang · Sanghamitra Dutta
Statistical Inference for Feature Selection after Optimal Transport-based Domain Adaptation Thang Loi Nguyen · Loc Duong · Vo Duy
Cost-aware simulation-based inference Ayush Bharti · Daolang Huang · Samuel Kaski · Francois-Xavier Briol
Robust Multi-fidelity Bayesian Optimization with Deep Kernel and Partition Fengxue Zhang · Thomas Desautels · Yuxin Chen
Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization Sudeep Salgia · Nikola Pavlovic · Yuejie Chi · Qing Zhao
Conditional Generative Learning from Invariant Representations in Multi-Source: Robustness and Efficiency Guojun Zhu · Sanguo Zhang · Mingyang Ren
Learning signals defined on graphs with optimal transport and Gaussian process regression Raphael Carpintero Perez · Sébastien da Veiga · Josselin Garnier · Brian Staber
Learning High-dimensional Gaussians from Censored Data Arnab Bhattacharyya · Constantinos Daskalakis · Themistoklis Gouleakis · Yuhao Wang
Meta-learning Task-specific Regularization Weights for Few-shot Linear Regression Tomoharu Iwata · Atsutoshi Kumagai · Yasutoshi Ida
Representer Theorems for Metric and Preference Learning: Geometric Insights and Algorithms Peyman Morteza
Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings Haolin Zou · Arnab Auddy · Kamiar Rahnama Rad · Arian Maleki
Infinite Width Limits of Self Supervised Neural Networks Maximilian Fleissner · Gautham Anil · Debarghya Ghoshdastidar
HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree Search Tuan Nguyen · Jay Barrett · Kwang-Sung Jun
Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis Remi Khellaf · Aurélien Bellet · julie Josse
Bayesian Principles Improve Prompt Learning In Vision-Language Models Mingyu Kim · Jongwoo Ko · Mijung Park
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees Arindam Banerjee · Qiaobo Li · Yingxue Zhou
On the Power of Multitask Representation Learning with Gradient Descent Qiaobo Li · Zixiang Chen · Yihe Deng · Yiwen Kou · Yuan Cao · Quanquan Gu
Diffusion Models under Group Transformations Haoye Lu · Spencer Szabados · Yaoliang Yu
Pure Exploration with Feedback Graphs Alessio Russo · Yichen Song · Aldo Pacchiano
Domain Adaptation and Entanglement: an Optimal Transport Perspective Okan Koc · Alexander Soen · Chao-Kai Chiang · Masashi Sugiyama
Energy-consistent Neural Operators for Hamiltonian and Dissipative Partial Differential Equations Yusuke Tanaka · Takaharu Yaguchi · Tomoharu Iwata · Naonori Ueda
Synthesis and Analysis of Data as Probability Measures With Entropy-Regularized Optimal Transport Brendan Mallery · James Murphy · Shuchin Aeron
Offline Multi-task Transfer RL with Representational Penalization Avinandan Bose · Simon Du · Maryam Fazel
Gaussian Mean Testing under Truncation Clement Canonne · Themistoklis Gouleakis · Yuhao Wang · Qiping Yang
Stein Boltzmann Sampling: A Variational Approach for Global Optimization Gaëtan Serré · Argyris Kalogeratos · Nicolas Vayatis
Cubic regularized subspace Newton for non-convex optimization Jim Zhao · Nikita Doikov · Aurelien Lucchi
UNHaP: Unmixing Noise from Hawkes Processes Virginie Loison · Guillaume Staerman · Thomas Moreau
On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients Satish Keshri · Nazreen Shah · Ranjitha Prasad
A Differential Inclusion Approach for Learning Heterogeneous Sparsity in Neuroimaging Analysis Wenjing Han · Yueming Wu · Xinwei Sun · Lingjing Hu · Yizhou Wang
Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach Dheeraj Baby · Boran Han · Shuai Zhang · Cuixiong Hu · Bernie Wang · Yu-Xiang Wang
Performative Reinforcement Learning with Linear Markov Decision Process Debmalya Mandal · Goran Radanovic
Sparse Causal Effect Estimation using Two-Sample Summary Statistics in the Presence of Unmeasured Confounding Shimeng Huang · Niklas Pfister · Jack Bowden
Independent Learning in Performative Markov Potential Games Rilind Sahitaj · Paulius Sasnauskas · Yiğit Yalın · Debmalya Mandal · Goran Radanovic
Memorization in Attention-only Transformers Léo Dana · Muni Sreenivas Pydi · Yann Chevaleyre
Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits Nicolas Nguyen · Imad Aouali · Andras Gyorgy · Claire Vernade
Flexible Copula-Based Mixed Models in Deep Learning: A Scalable Approach to Arbitrary Marginals Giora Simchoni · Saharon Rosset
Model selection for behavioral learning data and applications to contextual bandits Julien Aubert · Louis Köhler · Luc Lehéricy · Giulia Mezzadri · Patricia Reynaud-Bouret
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks Xin Liu · Weijia Zhang · Min-Ling Zhang
What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization Yufeng Zhang · Fengzhuo Zhang · Zhuoran Yang · Zhaoran Wang
Learning Visual-Semantic Subspace Representations Gabriel Moreira · Manuel Marques · Joao Costeira · Alexander Hauptmann
Distribution-Aware Mean Estimation under User-level Local Differential Privacy Corentin Pla · Maxime Vono · Hugo Richard
Bridging Multiple Worlds: Multi-marginal Optimal Transport for Causal Partial-identification Problem Zijun Gao · Shu Ge · Jian Qian
Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators NAICHANG KE · Ryogo Tanaka · Yoshinobu Kawahara
Bilevel Reinforcement Learning via the Development of Hyper-gradient without Lower-Level Convexity Yan Yang · Bin Gao · Ya-xiang Yuan
$f$ -PO: Generalizing Preference Optimization with $f$ -divergence Minimization Jiaqi Han · Mingjian Jiang · Yuxuan Song · Stefano Ermon · Minkai Xu
Cost-Aware Optimal Pairwise Pure Exploration Di Wu · Chengshuai Shi · Ruida Zhou · Cong Shen
Visualizing token importance for black-box language models Paulius Rauba · Qiyao Wei · Mihaela van der Schaar
Distributional Adversarial Loss Saba Ahmadi · Siddharth Bhandari · Avrim Blum · Chen Dan · Prabhav Jain
Learning the Pareto Front Using Bootstrapped Observation Samples Wonyoung Kim · Garud Iyengar · Assaf Zeevi
Scalable spectral representations for multiagent reinforcement learning in network MDPs Zhaolin Ren · Runyu Zhang · Bo Dai · Na Li
Transfer Learning for High-dimensional Reduced Rank Time Series Models Mingliang Ma · Abolfazl Safikhani
Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization Daniil Tiapkin · Evgenii Chzhen · Gilles Stoltz
Variance-Aware Linear UCB with Deep Representation for Neural Contextual Bandits Ha Manh Bui · Enrique Mallada · Anqi Liu
Classification of High-dimensional Time Series in Spectral Domain Using Explainable Features with Applications to Neuroimaging Data Sarbojit Roy · Malik Shahid Sultan · Tania Vallejo · Leena Ibrahim · Hernando Ombao
Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time Vladimir Braverman · Prathamesh Dharangutte · Shreyas Pai · Vihan Shah · Chen Wang
General Staircase Mechanisms for Optimal Differential Privacy Alex Kulesza · Ananda Theertha Suresh · Yuyan Wang
Algorithmic Accountability in Small Data: Sample-Size-Induced Bias Within Classification Metrics Jarren Briscoe · Garrett Kepler · Daryl DeFord · Assefaw Gebremedhin
Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VI Abhinav Agrawal · Justin Domke
RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation Yiming Wang · Yuxuan Song · Yiqun Wang · Minkai Xu · Rui Wang · Hao Zhou · Wei-Ying Ma
ROTI-GCV: Generalized Cross-Validation for right-ROTationally Invariant Data Kevin Luo · Yufan Li · Pragya Sur
How Well Can Transformers Emulate In-Context Newton's Method? Angeliki Giannou · Liu Yang · Tianhao Wang · Dimitris Papailiopoulos · Jason Lee
On the Computational Tractability of the (Many) Shapley Values Reda Marzouk · Shahaf Bassan · Guy Katz · De la Higuera
Theory of Agreement-on-the-Line in Linear Models and Gaussian Data Christina Baek · Aditi Raghunathan · Zico Kolter
Restructuring Tractable Probabilistic Circuits Honghua Zhang · Benjie Wang · Marcelo Arenas · Guy Van den Broeck
Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models Manan Saxena · Tinghua Chen · Justin Silverman
Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way Jeongyeol Kwon · Luke Dotson · Yudong Chen · Qiaomin Xie
Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation Paul Mangold · Alain Durmus · Aymeric Dieuleveut · Sergey Samsonov · Eric Moulines
Beyond Discretization: Learning the Optimal Solution Path Qiran Dong · Paul Grigas · Vishal Gupta
LITE: Efficiently Estimating Gaussian Probability of Maximality Nicolas Menet · Jonas Hübotter · Parnian Kassraie · Andreas Krause
Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning Boning Zhang · Dongzhu Liu · Osvaldo Simeone · Guanchu Wang · Dimitrios Pezaros · Guangxu Zhu
Rate of Model Collapse in Recursive Training Ananda Theertha Suresh · Andrew Thangaraj · Aditya Khandavally
Online-to-PAC generalization bounds under graph-mixing dependencies Baptiste Abélès · Gergely Neu · Eugenio Clerico
An Adaptive Method for Weak Supervision with Drifting Data Alessio Mazzetto · Reza Esfandiarpoor · Akash Singirikonda · Eli Upfal · Stephen Bach
Fixed-Budget Change Point Identification in Piecewise Constant Bandits Joseph Lazzaro · Ciara Pike-Burke
Koopman-Equivariant Gaussian Processes Petar Bevanda · Max Beier · Alexandre Capone · Stefan Sosnowski · Sandra Hirche · Armin Lederer
Level Set Teleportation: An Optimization Perspective Aaron Mishkin · Alberto Bietti · Robert Gower
Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPs Swetha Ganesh · Washim Uddin Mondal · Vaneet Aggarwal
On adaptivity and minimax optimality of two-sided nearest neighbors Tathagata Sadhukhan · Manit Paul · Raaz Dwivedi
Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory Lucas GNECCO HEREDIA · Matteo Sammut · Muni Sreenivas Pydi · Rafael Pinot · Benjamin Negrevergne · Yann Chevaleyre
Task Shift: From Classification to Regression in Overparameterized Linear Models Tyler LaBonte · Kuo-Wei Lai · Vidya Muthukumar
HR-Bandit: Human-AI Collaborated Linear Recourse Bandit Junyu Cao · Ruijiang Gao · Esmaeil Keyvanshokooh
Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits Brian Cho · Dominik Meier · Kyra Gan · Nathan Kallus
Privacy in Metalearning and Multitask Learning: Modeling and Separations Maryam Aliakbarpour · Konstantina Bairaktari · Adam Smith · Marika Swanberg · Jonathan Ullman
Multi-marginal Schrödinger Bridges with Iterative Reference Refinement Yunyi Shen · Renato Berlinghieri · Tamara Broderick
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory Zhi Zhang · Chris Chow · Yasi Zhang · Yanchao Sun · Haochen Zhang · Eric Jiang · Han Liu · Furong Huang · Yuchen Cui · Oscar Madrid
Rethinking Neural-based Matrix Inversion: Why can't, and Where can Yuliang Ji · Jian Wu · Yuanzhe Xi
Fairness Risks for Group-Conditionally Missing Demographics Kaiqi Jiang · Wenzhe Fan · Mao Li · Xinhua Zhang
Tensor Network-Constrained Kernel Machines as Gaussian Processes Frederiek Wesel · Kim Batselier
On Subjective Uncertainty Quantification and Calibration in Natural Language Generation Ziyu Wang · Chris Holmes
Integer Programming Based Methods and Heuristics for Causal Graph Learning Sanjeeb Dash · Joao Goncalves · Tian Gao
Nonparametric Factor Analysis and Beyond Yujia Zheng · Yang Liu · Jiaxiong Yao · Yingyao Hu · Kun Zhang
Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms Meltem Tatlı · Arpan Mukherjee · Prashanth L.A. · Karthikeyan Shanmugam · Ali Tajer
BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments Jordan Penn · Lee Gunderson · Gecia Bravo-Hermsdorff · Ricardo Silva · David Watson
Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz Constants Fares Fourati · Salma Kharrat · Vaneet Aggarwal · Mohamed-Slim Alouini
Steering No-Regret Agents in MFGs under Model Uncertainty Leo Widmer · Jiawei Huang · Niao He
A graphical global optimization framework for parameter estimation of statistical models with nonconvex regularization functions Danial Davarnia · Mohammadreza Kiaghadi
Gaussian Smoothing in Saliency Maps: The Stability-Fidelity Trade-Off in Neural Network Interpretability Zhuorui Ye · Farzan Farnia
Variation Due to Regularization Tractably Recovers Bayesian Deep Learning Uncertainty James McInerney · Nathan Kallus
Posterior Mean Matching: Generative Modeling through Online Bayesian Inference Sebastian Salazar · Michal Kucer · Yixin Wang · Emily Casleton · David Blei
Geometric Collaborative Filtering with Convergence Hisham Husain · Julien Monteil
Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems Da Long · Zhitong Xu · Qiwei Yuan · Yin Yang · Shandian Zhe
Deep Clustering via Probabilistic Ratio-Cut Optimization Ayoub Ghriss · Claire Monteleoni
A Novel Convex Gaussian Min Max Theorem for Repeated Features David Bosch · Ashkan Panahi
Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning Jiaru Zhang · Rui Ding · Qiang Fu · Huang Bojun · Zizhen Deng · Yang Hua · Haibing Guan · Shi Han · Dongmei Zhang
SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity Peihao Wang · Zhiwen Fan · Dejia Xu · Dilin Wang · Sreyas Mohan · Forrest Iandola · Rakesh Ranjan · YILEI LI · qiang liu · Zhangyang Wang · Vikas Chandra
High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching Daniel Williams · Leyang Wang · Qizhen Ying · Song Liu · Mladen Kolar
Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable Tim G. J. Rudner · Xiang Pan · Yucen Li · Ravid Shwartz-Ziv · Andrew Wilson
Nyström Kernel Stein Discrepancy Florian Kalinke · Zoltan Szabo · Bharath Sriperumbudur
Multi-level Advantage Credit Assignment for Cooperative Multi-Agent Reinforcement Learning Xutong Zhao · Yaqi Xie
Out-of-distribution robustness for multivariate analysis via causal regularisation Homer Durand · Gherardo Varando · Nathan Mankovich · Gustau Camps-Valls
An Iterative Algorithm for Rescaled Hyperbolic Functions Regression Yeqi Gao · Zhao Song · Junze Yin
Efficient Exploitation of Hierarchical Structure in Sparse Reward Reinforcement Learning Gianluca Drappo · Arnaud Robert · Marcello Restelli · Aldo Faisal · Alberto Maria Metelli · Ciara Pike-Burke
On the Geometry and Optimization of Polynomial Convolutional Networks Vahid Shahverdi · Giovanni Luca Marchetti · Kathlén Kohn
Linearized Wasserstein Barycenters: Synthesis, Analysis, Representational Capacity, and Applications Matthew Werenski · Brendan Mallery · Shuchin Aeron · James Murphy
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions Omer Noy Klein · Alihan Hüyük · Ron Shamir · Uri Shalit · Mihaela van der Schaar
Theoretical Convergence Guarantees for Variational Autoencoders Sobihan Surendran · Antoine Godichon-Baggioni · Sylvain Le Corff
Covariance Selection over Networks Wenfu Xia · Fengpei Li · Ying Sun · Ziping Zhao
Implicit Diffusion: Efficient optimization through stochastic sampling Pierre Marion · Anna Korba · Peter Bartlett · Mathieu Blondel · Valentin De Bortoli · Arnaud Doucet · Felipe Llinares-López · Courtney Paquette · Quentin Berthet
Harnessing Causality in Reinforcement Learning with Bagged Decision Times Daiqi Gao · Hsin-Yu Lai · Predrag Klasnja · Susan Murphy
When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time? Chenyang Li · Yingyu Liang · Zhenmei Shi · Zhao Song
Continuous Structure Constraint Integration for Robust Causal Discovery Lyuzhou Chen · Taiyu Ban · Derui Lyu · Yijia Sun · Kangtao Hu · Xiangyu Wang · Huanhuan Chen
Certifiably Quantisation-Robust training and inference of Neural Networks Hue Dang · Matthew Wicker · Goetz Botterweck · Andrea Patane
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling Xudong Sun · Nutan Chen · Alexej Gossmann · Yu Xing · Matteo Wohlrapp · Emilio Dorigatti · Carla Feistner · Felix Drost · Daniele Scarcella · Lisa Beer · Carsten Marr
The Sample Complexity of Stackelberg Games Francesco Bacchiocchi · Matteo Bollini · Matteo Castiglioni · Alberto Marchesi · Nicola Gatti
SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings Shivvrat Arya · Tahrima Rahman · Vibhav Gogate
Sampling from the Random Linear Model via Stochastic Localization Up to the AMP Threshold Han Cui · Zhiyuan Yu · Jingbo Liu
StableMDS: A Novel Gradient Descent-Based Method for Stabilizing and Accelerating Weighted Multidimensional Scaling Zhongxi Fang · Xun Su · Tomohisa Tabuchi · Jianming Huang · Hiroyuki Kasai
MING: A Functional Approach to Learning Molecular Generative Models Van Khoa NGUYEN · Maciej Falkiewicz · Giangiacomo Mercatali · Alexandros Kalousis
Information-Theoretic Causal Discovery in Topological Order Sascha Xu · Sarah Mameche · Jilles Vreeken
Empirical Error Estimates for Graph Sparsification Siyao Wang · Miles Lopes
Function-Space MCMC for Bayesian Wide Neural Networks Lucia Pezzetti · Stefano Favaro · Stefano Peluchetti
Causal Temporal Regime Structure Learning Abdellah Rahmani · Pascal Frossard
$\mathcal{I}$ -trustworthy Models. A framework for trustworthiness evaluation of probabilistic classifiers Ritwik Vashistha · Arya Farahi
Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits Ambrus Tamás · Szabolcs Szentpéteri · Balázs Csanád Csáji
A Multi-Armed Bandit Approach to Online Selection and Evaluation of Generative Models Xiaoyan Hu · Ho-fung Leung · Farzan Farnia
Variance-Dependent Regret Bounds for Nonstationary Linear Bandits Zhiyong Wang · Jize Xie · Yi Chen · John C. S. Lui · Dongruo Zhou
Changepoint Estimation in Sparse Dynamic Stochastic Block Models under Near-Optimal Signal Strength Shirshendu Chatterjee · Soumendu Sundar Mukherjee · Tamojit Sadhukhan
Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space Alex Chen · Philippe Chlenski · Kenneth Munyuza · Antonio Moretti · Christian Andersson Naesseth · Itsik Pe'er
Conditional diffusions for amortized neural posterior estimation Tianyu Chen · Vansh Bansal · James Scott
Post-processing for Fair Regression via Explainable SVD Zhiqun Zuo · Ding Zhu · Mahed Abroshan
Calibrated Computation-Aware Gaussian Processes Disha Hegde · Mohamed Adil · Jon Cockayne
Towards Fair Graph Learning without Demographic Information Zichong Wang · Nhat Hoang · Xingyu Zhang · Kevin Bello · Xiangliang Zhang · Sitharama Iyengar · Wenbin Zhang
On the Inherent Privacy of Zeroth-Order Projected Gradient Descent Devansh Gupta · Meisam Razaviyayn · Vatsal Sharan
Understanding the Learning Dynamics of LoRA: A Gradient Flow Perspective on Low-Rank Adaptation in Matrix Factorization Ziqing Xu · Hancheng Min · Lachlan MacDonald · Jinqi Luo · Salma Tarmoun · Enrique Mallada · Rene Vidal
Dissecting the Impact of Model Misspecification in Data-Driven Optimization Adam Elmachtoub · Henry Lam · Haixiang Lan · Haofeng Zhang
Analyzing Generative Models by Manifold Entropic Metrics Daniel Galperin · Ullrich Köthe
Parameter estimation in state space models using particle importance sampling Yuxiong Gao · Wentao Li · Rong Chen
Powerful batch conformal prediction for classification Ulysse Gazin · ruth heller · Etienne Roquain · Aldo Solari
Safe exploration in reproducing kernel Hilbert spaces Abdullah Tokmak · Kiran Krishnan · Thomas Schön · Dominik Baumann
Evaluating Prediction-based Interventions with Human Decision Makers In Mind Inioluwa Raji · Lydia Liu
Estimation of Large Zipfian Distributions with Sort and Snap Peter Jacobs · Anirban Bhattacharya · Debdeep Pati · Lekha Patel · Jeff Phillips
Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs? Son Luu · Zuheng Xu · Nikola Surjanovic · Miguel Biron-Lattes · Trevor Campbell · Alexandre Bouchard-Côté
A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration Yingqian Cui · Pengfei He · Xianfeng Tang · Qi He · Chen Luo · Jiliang Tang · Yue Xing
Hyperbolic Prototypical Entailment Cones for Image Classification Samuele Fonio · Roberto Esposito · Marco Aldinucci
Differentially private algorithms for linear queries via stochastic convex optimization Giorgio Micali · Clement Lezane · Annika Betken
A Likelihood Based Approach for Watermark Detection Xingchi Li · Guanxun Li · Xianyang Zhang
Learning Laplacian Positional Encodings for Heterophilous Graphs Michael Ito · Jiong Zhu · Dexiong Chen · Danai Koutra · Jenna Wiens
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs Enea Monzio Compagnoni · Rustem Islamov · Frank Proske · Aurelien Lucchi
On Tractability of Learning Bayesian Networks with Ancestral Constraints Juha Harviainen · Pekka Parviainen
AxlePro: Momentum-Accelerated Batched Training of Kernel Machines Yiming Zhang · Parthe Pandit
Pick-to-Learn and Self-Certified Gaussian Process Approximations Daniel Marks · Dario Paccagnan
MEDUSA: Medical Data Under Shadow Attacks via Hybrid Model Inversion Asfandyar Azhar · Paul Thielen · Curtis Langlotz
AlleNoise - large-scale text classification benchmark dataset with real-world label noise Alicja Rączkowska · Aleksandra Osowska-Kurczab · Jacek Szczerbiński · Kalina Jasinska-Kobus · Klaudia Nazarko
Multi-Player Approaches for Dueling Bandits Or Raveh · Junya Honda · Masashi Sugiyama
Adaptive Convergence Rates for Log-Concave Maximum Likelihood Gil Kur · Aditya Guntuboyina
Policy Teaching via Data Poisoning in Learning from Human Preferences Andi Nika · Jonathan Nöther · Debmalya Mandal · Parameswaran Kamalaruban · Adish Singla · Goran Radanovic
Active Feature Acquisition for Personalised Treatment Assignment Julianna Piskorz · Nicolás Astorga · Jeroen Berrevoets · Mihaela van der Schaar
Near-optimal algorithms for private estimation and sequential testing of collision probability Robert Busa-Fekete · Umar Syed
An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and Applications Erfan Mirzaei · Andreas Maurer · Vladimir Kostic · Massimiliano Pontil
Models That Are Interpretable But Not Transparent Chudi Zhong · Panyu Chen · Cynthia Rudin
The Strong Product Model for Network Inference without Independence Assumptions Bailey Andrew · David Westhead · Luisa Cutillo
Optimistic Safety for Online Convex Optimization with Unknown Linear Constraints Spencer Hutchinson · Tianyi Chen · Mahnoosh Alizadeh
The VampPrior Mixture Model Andrew Stirn · David Knowles
TempTest: Local Normalization Distortion and the Detection of Machine-generated Text Tom Kempton · Stuart Burrell · Connor Cheverall
Robust Gradient Descent for Phase Retrieval Alex Buna · Patrick Rebeschini
Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces Shengbo Wang · NIAN SI · Jose Blanchet · Zhengyuan Zhou
High Dimensional Bayesian Optimization using Lasso Variable Selection Vu Hoang · Hung Tran · Sunil Gupta · Vu Nguyen
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning Khimya Khetarpal · Zhaohan Daniel Guo · Bernardo Avila Pires · Yunhao Tang · Clare Lyle · Mark Rowland · Nicolas Heess · Diana Borsa · Arthur Guez · Will Dabney
Prediction-Centric Uncertainty Quantification via MMD Zheyang Shen · Jeremias Knoblauch · Sam Power · Chris Oates
Distributional Off-policy Evaluation with Bellman Residual Minimization Sungee Hong · Zhengling Qi · Raymond K. W. Wong
Fourier Circuits in Neural Networks and Transformers: A Case Study of Modular Arithmetic with Multiple Inputs Chenyang Li · Yingyu Liang · Zhenmei Shi · Zhao Song · Tianyi Zhou
Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect Ojash Neopane · Aaditya Ramdas · Aarti Singh
Stochastic Rounding for LLM Training: Theory and Practice Kaan Ozkara · Tao Yu · Youngsuk Park
Learning Graph Node Embeddings by Smooth Pair Sampling Konstantin Kutzkov
FreqMoE: Enhancing Time Series Forecasting through Frequency Decomposition Mixture of Experts ziqi Liu
Adaptive Extragradient Methods for Root-finding Problems under Relaxed Assumptions Yang Luo · Michael O'Neill
Feasible Learning Juan Ramirez · Ignacio Hounie · Juan Elenter · Jose Gallego-Posada · Meraj Hashemizadeh · Alejandro Ribeiro · Simon Lacoste-Julien
Bandit Pareto Set Identification in a Multi-Output Linear Model Cyrille Kone · Emilie Kaufmann · Laura Richert
Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible? Argyrios Gerogiannis · Yu-Han Huang · Venugopal V. Veeravalli
A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities Yatin Dandi · Luca Pesce · Hugo Cui · FLORENT KRZAKALA · Yue Lu · Bruno Loureiro
Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects Seyedeh Khatami · Harsh Parikh · Haowei Chen · Sudeepa Roy · Babak Salimi
Automatically Adaptive Conformal Risk Control Vincent Blot · Anastasios Angelopoulos · Michael Jordan · Nicolas Brunel
DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows Jonathan Geuter · Clément Bonet · Anna Korba · David Alvarez-Melis
A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning Chungpa Lee · Jeongheon Oh · Kibok Lee · Jy-yong Sohn
Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models Siyan Zhao · Daniel Israel · Guy Van den Broeck · Aditya Grover
Explaining ViTs Using Information Flow Chase Walker · Md Rubel Ahmed · Sumit Kumar Jha · Rickard Ewetz
A Shapley-value Guided Rationale Editor for Rationale Learning Zixin Kuang · Meng-Fen Chiang · Wang-Chien Lee
Counting Graphlets of Size k under Local Differential Privacy Vorapong Suppakitpaisarn · Donlapark Ponnoprat · Nicha Hirankarn · Quentin Hillebrand
Clustering Context in Off-Policy Evaluation Daniel Guzmán Olivares · Philipp Schmidt · Jacek Golebiowski · Artur Bekasov
Tight Analysis of Difference-of-Convex Algorithm (DCA) Improves Convergence Rates for Proximal Gradient Descent Teodor Rotaru · Panagiotis Patrinos · François Glineur
Disentangling Interactions and Dependencies in Feature Attributions Gunnar König · Eric Günther · Ulrike von Luxburg
Spectral Differential Network Analysis for High-Dimensional Time Series Michael Hellstern · Byol Kim · Zaid Harchaoui · Ali Shojaie
Generalization Bounds for Dependent Data using Online-to-Batch Conversion. Sagnik Chatterjee · MANUJ MUKHERJEE · Alhad Sethi
Differentiable Calibration of Inexact Stochastic Simulation Models via Kernel Score Minimization Ziwei Su · Diego Klabjan
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents Safwan Labbi · Daniil Tiapkin · Lorenzo Mancini · Paul Mangold · Eric Moulines
Training Neural Samplers with Reverse Diffusive KL Divergence Jiajun He · Wenlin Chen · Mingtian Zhang · David Barber · Jose Miguel Hernandez-Lobato
What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much? Rafał Karczewski · Samuel Kaski · Markus Heinonen · Vikas Garg
ScoreFusion: Fusing Score-based Generative Models via Kullback–Leibler Barycenters Hao Liu · Junze Ye · Jose Blanchet · NIAN SI
Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problem Katherine Tieu · Dongqi Fu · Jun Wu · Jingrui He
On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark Jaiden Fairoze · Guillermo Ortiz-Jimenez · Mel Vecerik · Somesh Jha · Sven Gowal
Time-varying Gaussian Process Bandits with Unknown Prior Juliusz Ziomek · Masaki Adachi · Michael A. Osborne
Max-Rank: Efficient Multiple Testing for Conformal Prediction Alexander Timans · Christoph-Nikolas Straehle · Kaspar Sakmann · Christian Andersson Naesseth · Eric Nalisnick
$\beta$ -th order Acyclicity Derivatives for DAG Learning Madhumitha Shridharan · Garud Iyengar
Optimal Multi-Objective Best Arm Identification with Fixed Confidence Zhirui Chen · P. N. Karthik · Yeow Meng Chee · Vincent Tan
A Safe Bayesian Learning Algorithm for Constrained MDPs with Bounded Constraint Violation Krishna Kalagarla · Rahul Jain · Pierluigi Nuzzo
Stochastic Weight Sharing for Bayesian Neural Networks Moule Lin · Shuhao Guan · Weipeng Jing · Goetz Botterweck · Andrea Patane
Dynamic DBSCAN with Euler Tour Sequences Seiyun Shin · Ilan Shomorony · Peter Macgregor
Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization Feihu Huang · Chunyu Xuan · Xinrui Wang · Siqi Zhang · Songcan Chen
Information Transfer Across Clinical Tasks via Adaptive Parameter Optimisation Anshul Thakur · Elena Gal · Soheila Molaei · Xiao Gu · Patrick Schwab · Danielle Belgrave · Kim Branson · David Clifton
Near-Polynomially Competitive Active Logistic Regression Yihan Zhou · Eric Price · Trung Nguyen
Robust Kernel Hypothesis Testing under Data Corruption Antonin Schrab · Ilmun Kim
Deep Generative Quantile Bayes Jungeum Kim · Percy Zhai · Veronika Rockova
Fundamental Limits of Perfect Concept Erasure Somnath Basu Roy Chowdhury · Kumar Avinava Dubey · Ahmad Beirami · Rahul Kidambi · Nicholas Monath · Amr Ahmed · Snigdha Chaturvedi
Tighter Confidence Bounds for Sequential Kernel Regression Hamish Flynn · David Reeb
Provable Benefits of Task-Specific Prompts for In-context Learning Xiangyu Chang · Yingcong Li · Muti Kara · Samet Oymak · Amit Roy-Chowdhury
Natural Language Counterfactual Explanations for Graphs Using Large Language Models Flavio Giorgi · Cesare Campagnano · Fabrizio Silvestri · Gabriele Tolomei
Local Stochastic Sensitivity Analysis For Dynamical Systems Nishant Panda · Jehanzeb Chaudhry · Natalie Klein · James Carzon · Troy Butler
Lower Bounds for Time-Varying Kernelized Bandits Xu Cai · Jonathan Scarlett
A primer on linear classification with missing data Angel David Reyero Lobo · Alexis Ayme · Claire Boyer · Erwan Scornet
Global Ground Metric Learning with Applications to scRNA data Damin Kühn · Michael Schaub
Generalization Lower Bounds for GD and SGD in Smooth Stochastic Convex Optimization Peiyuan Zhang · Jiaye Teng · Jingzhao Zhang
Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networks Samuel Tesfazgi · Leonhard Sprandl · Sandra Hirche
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models Xinxing Shi · Thomas Baldwin-McDonald · Mauricio Álvarez
Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings Nikolaos Nakis · CHRYSOULA KOSMA · Giannis Nikolentzos · Michail Chatzianastasis · Iakovos Evdaimon · Michalis Vazirgiannis
Differentially Private Continual Release of Histograms and Related Queries Monika Henzinger · A. R. Sricharan · Teresa Steiner
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation Amartya Sanyal · Yaxi Hu · Yaodong Yu · Yian Ma · Yixin Wang · Bernhard Schölkopf
Optimal downsampling for Imbalanced Classification with Generalized Linear Models Yan Chen · Jose Blanchet · Krzysztof Dembczynski · Laura F. Nern · Aaron Flores
Scalable Out-of-Distribution Robustness in the Presence of Unobserved Confounders Parjanya Prashant · Seyedeh Khatami · Bruno Ribeiro · Babak Salimi
Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks Ashwin Samudre · Mircea Petrache · Brian Nord · Shubhendu Trivedi
Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control Zifan LIU · Xinran Li · Shibo Chen · Gen Li · Jiashuo Jiang · Jun Zhang
On the Identifiability of Causal Abstractions Xiusi Li · Sékou-Oumar Kaba · Siamak Ravanbakhsh
Fundamental computational limits of weak learnability in high-dimensional multi-index models Emanuele Troiani · Yatin Dandi · Leonardo Defilippis · Lenka Zdeborova · Bruno Loureiro · FLORENT KRZAKALA
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation Diantong Li · Fengxue Zhang · Chong Liu · Yuxin Chen
Optimising Clinical Federated Learning through Mode Connectivity-based Model Aggregation Anshul Thakur · Soheila Molaei · Patrick Schwab · Danielle Belgrave · Kim Branson · David Clifton
Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span Woojin Chae · Kihyuk (Ki) Hong · Yufan Zhang · Ambuj Tewari · Dabeen Lee
Sparse Activations as Conformal Predictors Margarida Campos · João Cálem · Sophia Sklaviadis · Mario Figueiredo · Andre Martins
To Give or Not to Give? The Impacts of Strategically Withheld Recourse Yatong Chen · Andrew Estornell · Yevgeniy Vorobeychik · Yang Liu
Linear Submodular Maximization with Bandit Feedback Wenjing Chen · Victoria Crawford
Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization Simon Vary · David Martínez-Rubio · Patrick Rebeschini
Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation Yilin Xie · Shiqiang Zhang · Joel Paulson · Calvin Tsay
Approximate information maximization for bandit games Alex Chebbah · Christian L. Vestergaard · jean-baptiste masson · Etienne Boursier
Epistemic Uncertainty and Excess Risk in Variational Inference Futoshi Futami
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs Kasimir Tanner · Matteo Vilucchio · Bruno Loureiro · FLORENT KRZAKALA
Planning and Learning in Risk-Aware Restless Multi-Arm Bandits Nima Akbarzadeh · Yossiri Adulyasak · Erick Delage
Anytime-Valid A/B Testing of Counting Processes Michael Lindon · Nathan Kallus
Credal Two-Sample Tests of Epistemic Uncertainty Siu Lun Chau · Antonin Schrab · Arthur Gretton · Dino Sejdinovic · Krikamol Muandet
Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory Fabian Fumagalli · Maximilian Muschalik · Eyke Hüllermeier · Barbara Hammer · Julia Herbinger
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation Wenyuan Zhao · Haoyuan Chen · Tie Liu · Rui Tuo · Chao Tian
Primal-Dual Spectral Representation for Off-policy Evaluation Yang Hu · Tianyi Chen · Na Li · Kai Wang · Bo Dai
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model Francesco Pezzicoli · Valentina Ros · Francois Landes · Marco Baity-Jesi
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks Felix Jimenez · Matthias Katzfuss
Hypernym Bias: Unraveling Deep Classifier Training Dynamics through the Lens of Class Hierarchy Roman Malashin · Valeria Yachnaya · Alexandr Mullin
Consistent Amortized Clustering via Generative Flow Networks Irit Chelly · Roy Uziel · Oren Freifeld · Ari Pakman
Efficient Trajectory Inference in Wasserstein Space Using Consecutive Averaging Amartya Banerjee · Lee · Nir Sharon · Caroline Moosmüller
Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners Yuxin Wang · Botian Jiang · Yiran Guo · Quan Gan · David Wipf · Xuanjing Huang · Xipeng Qiu
Almost linear time differentially private release of synthetic graphs Zongrui Zou · Jingcheng Liu · Jalaj Upadhyay
Federated Communication-Efficient Multi-Objective Optimization Baris Askin · Pranay Sharma · Gauri Joshi · Carlee Joe-Wong
Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs Krzysztof Choromanski · Isaac Reid · Arijit Sehanobish · Kumar Avinava Dubey
ClusterSC: Advancing Synthetic Control with Donor Selection Saeyoung Rho · Andrew Tang · Noah Bergam · Rachel Cummings · Vishal Misra
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints Lingkai Kong · Yuanqi Du · Wenhao Mu · Kirill Neklyudov · Valentin De Bortoli · Dongxia Wu · Haorui Wang · Aaron Ferber · Yian Ma · Carla Gomes · Chao Zhang
Computation-Aware Kalman Filtering and Smoothing Marvin Pförtner · Jonathan Wenger · Jon Cockayne · Philipp Hennig
Protein Fitness Landscape: Spectral Graph Theory Perspective HAO ZHU · Daniel M. Steinberg · Piotr Koniusz
posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms Måns Magnusson · Jakob Torgander · Paul Bürkner · Lu Zhang · Bob Carpenter · Aki Vehtari
Towards a mathematical theory for consistency training in diffusion models Gen Li · Zhihan Huang · Yuting Wei
Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel–Young Loss Perspective and Gap-Dependent Regret Analysis Shinsaku Sakaue · Han Bao · Taira Tsuchiya
Conditioning diffusion models by explicit forward-backward bridging Adrien Corenflos · Zheng Zhao · Thomas Schön · Simo Särkkä · Jens Sjölund
LMEraser: Large Model Unlearning via Adaptive Prompt Tuning Jie Xu · Zihan Wu · Cong Wang · Xiaohua Jia
Emergence of Globally Attracting Fixed Points in Deep Neural Networks With Nonlinear Activations Amir Joudaki · Thomas Hofmann
Leveraging Frozen Batch Normalization for Co-Training in Source-Free Domain Adaptation Xianwen Deng · Yijun Wang · Zhi Xue
Active Bipartite Ranking with Smooth Posterior Distributions James Cheshire · Stephan Clemencon
Efficient Optimization Algorithms for Linear Adversarial Training Antonio Ribeiro · Thomas Schön · Dave Zachariah · Francis Bach
Parabolic Continual Learning Haoming Yang · Ali Hasan · Vahid Tarokh
Learning to Negotiate via Voluntary Commitment Shuhui Zhu · Baoxiang Wang · Sriram Ganapathi Subramanian · Pascal Poupart
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference Dai Hai Nguyen · Tetsuya Sakurai · Hiroshi Mamitsuka
Trustworthy assessment of heterogeneous treatment effect estimator via analysis of relative error Zijun Gao
Elastic Representation: Mitigating Spurious Correlations for Group Robustness Tao Wen · Zihan Wang · Quan Zhang · Qi Lei
Strategic Conformal Prediction Daniel Csillag · Claudio Struchiner · Guilherme Goedert
Proximal Sampler with Adaptive Step Size Bo Yuan · Jiaojiao Fan · Jiaming Liang · Yongxin Chen
Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks Julie Alberge · Vincent Maladiere · Olivier Grisel · Judith Abécassis · Gaël Varoquaux
Transfer Neyman-Pearson Algorithm for Outlier Detection Mohammadreza Mousavi Kalan · Eitan Neugut · Samory Kpotufe
Your copula is a classifier in disguise: classification-based copula density estimation David Huk · Mark Steel · Ritabrata Dutta
Multi-Agent Credit Assignment with Pretrained Language Models Wenhao Li · Dan Qiao · Baoxiang Wang · Xiangfeng Wang · Wei Yin · Hao Shen · Bo Jin · Hongyuan Zha
Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoning Avinandan Bose · Laurent Lessard · Maryam Fazel · Krishnamurthy Dvijotham
Causal discovery in mixed additive noise models Ruicong Yao · Tim Verdonck · Jakob Raymaekers
Microfoundation inference for strategic prediction Daniele Bracale · Subha Maity · Felipe Maia Polo · Seamus Seamus · Moulinath Banerjee · Yuekai Sun
Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence Berfin Simsek · Amire Bendjeddou · Daniel Hsu
Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems Mikołaj Słupiński
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders Christian Toth · Christian Knoll · Franz Pernkopf · Robert Peharz
Synthetic Potential Outcomes and Causal Mixture Identifiability Bijan Mazaheri · Chandler Squires · Caroline Uhler
Infinite-dimensional Diffusion Bridge Simulation via Operator Learning Gefan Yang · Baker · Michael Severinsen · Christy Hipsley · Stefan Sommer
Legitimate ground-truth-free metrics for deep uncertainty classification scoring Arthur Pignet · Chiara Regniez · John Klein
Faster WIND: Accelerating Iterative Best-of- $N$ Distillation for LLM Alignment Tong Yang · Jincheng Mei · Hanjun Dai · Zixin Wen · Shicong Cen · Dale Schuurmans · Yuejie Chi · Bo Dai
Differential Privacy in Distributed Learning: Beyond Uniformly Bounded Stochastic Gradients Yue Huang · Jiaojiao Zhang · Qing Ling
Amortized Probabilistic Conditioning for Optimization, Simulation and Inference Paul Chang · Nasrulloh Loka · Daolang Huang · Ulpu Remes · Samuel Kaski · Luigi Acerbi
On the Asymptotic Mean Square Error Optimality of Diffusion Models Benedikt Fesl · Benedikt Böck · Florian Strasser · Michael Baur · Michael Joham · Wolfgang Utschick
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift Baozhen Wang · Xingye Qiao
Causal Representation Learning from General Environments under Nonparametric Mixing Ignavier Ng · Shaoan Xie · Xinshuai Dong · Peter Spirtes · Kun Zhang
Minimum Empirical Divergence for Sub-Gaussian Linear Bandits Kapilan Balagopalan · Kwang-Sung Jun
Learning in Herding Mean Field Games: Single-Loop Algorithm with Finite-Time Convergence Analysis Sihan Zeng · Sujay Bhatt · Alec Koppel · Sumitra Ganesh
Bridging the Theoretical Gap in Randomized Smoothing Blaise Delattre · Paul Caillon · Quentin Barthélemy · Erwan Fagnou · Alexandre Allauzen
Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem Shaan Ul Haque · Siva Theja Maguluri
Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy Maryam Aliakbarpour · Syomantak Chaudhuri · Thomas Courtade · Alireza Fallah · Michael Jordan
Subspace Recovery in Winsorized PCA: Insights into Accuracy and Robustness Sangil Han · Kyoowon Kim · Sungkyu Jung
Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes Csaba Tóth · Masaki Adachi · Michael A. Osborne · Harald Oberhauser
Perfect Recovery for Random Geometric Graph Matching with Shallow Graph Neural Networks Suqi Liu · Morgane Austern
Get rid of your constraints and reparametrize: A study in NNLS and implicit bias Hung-Hsu Chou · Johannes Maly · Claudio Mayrink Verdun · Bernardo da Costa · Heudson Mirandola
DPFL: Decentralized Personalized Federated Learning Salma Kharrat · Marco Canini · Samuel Horvath
A Shared Low-Rank Adaptation Approach to Personalized RLHF Renpu Liu · Peng Wang · Donghao Li · Cong Shen · Jing Yang
Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States Han Bao · Shinsaku Sakaue
Multimodal Learning with Uncertainty Quantification based on Discounted Belief Fusion Grigor Bezirganyan · Sana Sellami · Laure Berti-Equille · Sébastien Fournier
When the Universe is Too Big: Bounding Consideration Probabilities for Plackett-Luce Rankings Ben Aoki-Sherwood · Catherine Bregou · David Liben-Nowell · Kiran Tomlinson · Thomas Zeng
SubSearch: Robust Estimation and Outlier Detection for Stochastic Block Models via Subgraph Search Leonardo Martins Bianco · Christine Keribin · Zacharie Naulet
Personalized Convolutional Dictionary Learning of Physiological Time Series Axel Roques · Samuel Gruffaz · Kyurae Kim · Alain Durmus · Laurent Oudre
Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data Chengrui Qu · Laixi Shi · Kishan Panaganti · Pengcheng You · Adam Wierman
Nonparametric Distributional Regression via Quantile Regression Cheng Peng · Stan Uryasev
Corruption Robust Offline Reinforcement Learning with Human Feedback Debmalya Mandal · Andi Nika · Parameswaran Kamalaruban · Adish Singla · Goran Radanovic
Tensor Network Based Feature Learning Model Albert Saiapin · Kim Batselier
Understanding Expert Structures on Minimax Parameter Estimation in Contaminated Mixture of Experts Fanqi Yan · Huy Nguyen · Le Dung · Pedram Akbarian Saravi · Nhat Ho
The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size Control Mathieu Besançon · Sebastian Pokutta · Elias Wirth
Behavior-Inspired Neural Networks for Relational Inference Yulong Yang · Bowen Feng · Keqin Wang · Naomi Leonard · Adji Bousso Dieng · Christine Allen-Blanchette
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds Xingzhi Sun · Danqi Liao · Kincaid MacDonald · Yanlei Zhang · Guillaume Huguet · Guy Wolf · Ian Adelstein · Tim G. J. Rudner · Smita Krishnaswamy
QuACK: A Multipurpose Queuing Algorithm for Cooperative $k$ -Armed Bandits Benjamin Howson · Sarah Filippi · Ciara Pike-Burke
A Tight Regret Analysis of Non-Parametric Repeated Contextual Brokerage Francois Bachoc · Tommaso Cesari · Roberto Colomboni
Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses Mathieu Bazinet · Valentina Zantedeschi · Pascal Germain
On Preference-based Stochastic Linear Contextual Bandits with Knapsacks Xin Liu
Robust Classification by Coupling Data Mollification with Label Smoothing Markus Heinonen · Ba-Hien Tran · Michael Kampffmeyer · Maurizio Filippone
Ordered $\mathcal{V}$ -information Growth: A Fresh Perspective on Shared Information Rohan Ghosh · Mehul Motani
MDP Geometry, Normalization and Reward Balancing Solvers Arsenii Mustafin · Aleksei Pakharev · Alex Olshevsky · Yannis Paschalidis
Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent Bo Chen · Xiaoyu Li · Yingyu Liang · Zhenmei Shi · Zhao Song
Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete Optimization Ankur Nath · Alan Kuhnle
Do Regularization Methods for Shortcut Mitigation Work As Intended? Haoyang Hong · Ioanna Papanikolaou · Sonali Parbhoo
On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors Tim Rensmeyer · Oliver Niggemann
Causal Discovery on Dependent Binary Data Alex Chen · Qing Zhou
Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections Marco Miani · Hrittik Roy · Soren Hauberg
Approximating the Total Variation Distance between Gaussians Arnab Bhattacharyya · Weiming Feng · Piyush Srivastava
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector Andi Zhang · Tim Xiao · Weiyang Liu · Robert Bamler · Damon Wischik
Nonparametric estimation of Hawkes processes with RKHSs Anna Bonnet · Maxime Sangnier
InnerThoughts: Disentangling Representations and Predictions in Large Language Models Didier Chételat · Joseph Cotnareanu · Rylee Thompson · Yingxue Zhang · Mark Coates
Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization Deep Chakraborty · Yann LeCun · Tim G. J. Rudner · Erik Learned-Miller
Regularity in Canonicalized Models: A Theoretical Perspective Behrooz Tahmasebi · Stefanie Jegelka
Large Covariance Matrix Estimation With Nonnegative Correlations Yixin Yan · QIAO YANG · Ziping Zhao
Contractivity and linear convergence in bilinear saddle-point problems: An operator-theoretic approach Colin Dirren · Mattia Bianchi · Panagiotis D. Grontas · John Lygeros · Florian Dorfler
Importance-weighted Positive-unlabeled Learning for Distribution Shift Adaptation Atsutoshi Kumagai · Tomoharu Iwata · Hiroshi Takahashi · Taishi Nishiyama · Yasuhiro Fujiwara
Online Student- $t$ Processes with an Overall-local Scale Structure for Modelling Non-stationary Data Taole Sha · Michael Zhang
Bridging Domains with Approximately Shared Features Ziliang Zhong · Xiang Pan · Qi Lei
Causal Discovery-Driven Change Point Detection in Time Series Shanyun Gao · Raghavendra Addanki · Tong Yu · Ryan Rossi · Murat Kocaoglu
Truncated Inverse-Lévy Measure Representation of the Beta Process Junyi ZHANG · Angelos Dassios · Zhong Chong · Qiufei Yao
Collaborative non-parametric two-sample testing Alejandro de la Concha · Nicolas Vayatis · Argyris Kalogeratos
Cross-Modal Imputation and Uncertainty Estimation for Spatial Transcriptomics Xiangyu Guo · Ricardo Henao
On the Sample Complexity of Next-Token Prediction Oguz Kaan Yüksel · Nicolas Flammarion
FedBaF: Federated Learning Aggregation Biased by a Foundation Model Jong-Ik Park · Srinivasa Pranav · José Moura · Carlee Joe-Wong
Data Reconstruction Attacks and Defenses: A Systematic Evaluation Sheng Liu · Zihan Wang · Yuxiao Chen · Qi Lei
Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps Ricardo Baptista · Aram-Alexandre Pooladian · Michael Brennan · Youssef Marzouk · Jonathan Niles-Weed
Structure based SAT dataset for analysing GNN generalisation Yi Fu · Anthony Tompkins · Yang Song · Maurice Pagnucco
From Gradient Clipping to Normalization for Heavy Tailed SGD Florian Hübler · Ilyas Fatkhullin · Niao He
All models are wrong, some are useful: Model Selection with Limited Labels Patrik Okanovic · Andreas Kirsch · Jannes Kasper · Torsten Hoefler · Andreas Krause · Nezihe Merve Gürel
Variational Schr\"odinger Momentum Diffusion Kevin Rojas · Yixin Tan · Molei Tao · Yuriy Nevmyvaka · Wei Deng
Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis Jia Lin Hau · Erick Delage · Esther Derman · Mohammad Ghavamzadeh · Marek Petrik
Superiority of Multi-Head Attention: A Theoretical Study in Shallow Transformers in In-Context Linear Regression Yingqian Cui · Jie Ren · Pengfei He · Hui Liu · Jiliang Tang · Yue Xing
From Learning to Optimize to Learning Optimization Algorithms Camille Castera · Peter Ochs
Fast Convergence of Softmax Policy Mirror Ascent Reza Asad · Reza Babanezhad · Issam Hadj Laradji · Nicolas Le Roux · Sharan Vaswani
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics Daniel Paulin · Peter Whalley · Neil Chada · Benedict Leimkuhler
Signal Recovery from Random Dot-Product Graphs under Local Differential Privacy Siddharth Vishwanath · Jonathan Hehir
Randomized Iterative Solver as Iterative Refinement: A Simple Fix Towards Backward Stability Ruihan Xu · Yiping Lu
Copula Based Trainable Calibration Error Estimator of Multi-Label Classification with Label Interdependencies Arkapal Panda · Utpal Garain
MODL: Multilearner Online Deep Learning Antonios Valkanas · Boris Oreshkin · Mark Coates
Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing Flows Sandeep Nagar · Girish Varma
Fair Resource Allocation in Weakly Coupled Markov Decision Processes Xiaohui Tu · Yossiri Adulyasak · Nima Akbarzadeh · Erick Delage
Differentially Private Range Queries with Correlated Input Perturbation Prathamesh Dharangutte · Jie Gao · Ruobin Gong · Guanyang Wang
$q\texttt{POTS}$ : Efficient Batch Multiobjective Bayesian Optimization via Pareto Optimal Thompson Sampling Ashwin Renganathan · Kade Carlson
M $^2$ AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding Sarah Alnegheimish · Zelin He · Matthew Reimherr · Akash Chandrayan · Abhinav Pradhan · Luca D'Angelo
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits Masahiro Kato · Shinji Ito
Evidential Uncertainty Probes for Graph Neural Networks Linlin Yu · Kangshuo Li · Pritom Saha · Yifei Lou · Feng Chen
Batch, match, and patch: low-rank approximations for score-based variational inference Chirag Modi · Diana Cai · Lawrence Saul
Towards Cost Sensitive Decision Making Yang Li · Junier Oliva
A Subquadratic Time Approximation Algorithm for Individually Fair k-Center Matthijs Ebbens · Nicole Funk · Jan Höckendorff · Christian Sohler · Vera Weil
Multi-agent Multi-armed Bandit Regret Complexity and Optimality Mengfan Xu · Diego Klabjan
Signature Isolation Forest Marta Campi · Guillaume Staerman · Gareth Peters · Tomoko Masui
Partial Information Decomposition for Data Interpretability and Feature Selection Charles Westphal · Stephen Hailes · Mirco Musolesi
Calm Composite Losses: Being Improper Yet Proper Composite Han Bao · Nontawat Charoenphakdee
Robust Estimation in metric spaces: Achieving Exponential Concentration with a Fr\'echet Median Jakwang Kim · Jiyoung Park · Anirban Bhattacharya
Variational Adversarial Training Towards Policies with Improved Robustness Juncheng Dong · Hao-Lun Hsu · Qitong Gao · Vahid Tarokh · Miroslav Pajic
Weighted Euclidean Distance Matrices over Mixed Continuous and Categorical Inputs for Gaussian Process Models Mingyu Pu · Songhao Wang · Haowei Wang · Szu Hui Ng
Infinite-Horizon Reinforcement Learning with Multinomial Logit Function Approximation Jaehyun Park · Junyeop Kwon · Dabeen Lee
Application of Structured State Space Models to High energy physics with locality sensitive hashing Cheng Jiang · Sitian Qian
Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-Calibration Alexandre Perez-Lebel · Gaël Varoquaux · Sanmi Koyejo · Matthieu Doutreligne · Marine Le Morvan
Type Information-Assisted Self-Supervised Knowledge Graph Denoising Jiaqi Sun · Yujia Zheng · Xinshuai Dong · Haoyue Dai · Kun Zhang
Functional Stochastic Gradient MCMC for Bayesian Neural Networks Mengjing Wu · Junyu Xuan · Jie Lu
Time-series attribution maps with regularized contrastive learning Steffen Schneider · Rodrigo González Laiz · Anastasiia Filippova · Markus Frey · Mackenzie Mathis
Credibility-Aware Multimodal Fusion Using Probabilistic Circuits Sahil Sidheekh · Pranuthi Tenali · Saurabh Mathur · Erik Blasch · Kristian Kersting · Sriraam Natarajan
Knowledge Graph Completion with Mixed Geometry Tensor Factorization Viacheslav Yusupov · Maxim Rakhuba · Evgeny Frolov
Sampling From Multiscale Densities With Delayed Rejection Generalized Hamiltonian Monte Carlo Gilad Turok · Chirag Modi · Bob Carpenter
Bayesian Decision Theory on Decision Trees: Uncertainty Evaluation and Interpretability Yuta Nakahara · Shota Saito · Naoki Ichijo · Koki Kazama · Toshiyasu Matsushima
Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model Zilong Deng · Simon Khan · Shaofeng Zou
ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables Sebastian Pineda Arango · Pedro Mercado · Shubham Kapoor · Abdul Fatir Ansari · Lorenzo Stella · Huibin Shen · Hugo Sénétaire · Caner Turkmen · Oleksandr Shchur · Danielle Maddix · Michael Bohlke-Schneider · Bernie Wang · Syama Sundar Rangapuram
Memory-Efficient Optimization with Factorized Hamiltonian Descent Son Nguyen · Lizhang Chen · Bo Liu · qiang liu
Entropic Matching for Expectation Propagation of Markov Jump Processes Yannick Eich · Bastian Alt · Heinz Koeppl
On the Relationship Between Robustness and Expressivity of Graph Neural Networks Lorenz Kummer · Wilfried Gansterer · Nils Kriege
Information-Theoretic Measures on Lattices for Higher-Order Interactions Zhaolu Liu · Mauricio Barahona · Robert Peach
Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances Xuefeng GAO · Lingjiong Zhu
Mean-Field Microcanonical Gradient Descent Marcus Häggbom · Morten Karlsmark · Joakim Andén
Deep Optimal Sensor Placement for Black Box Stochastic Simulations Paula Cordero Encinar · Tobias Schroeder · Peter Yatsyshin · Andrew Duncan
A Unified Evaluation Framework for Epistemic Predictions Shireen Kudukkil Manchingal · Muhammad Mubashar · Kaizheng Wang · Fabio Cuzzolin
Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo James Thornton · Louis Béthune · Ruixiang ZHANG · Arwen Bradley · Preetum Nakkiran · Shuangfei Zhai
TVineSynth: A Truncated C-Vine Copula Generator of Synthetic Tabular Data to Balance Privacy and Utility Elisabeth Griesbauer · Claudia Czado · Arnoldo Frigessi · Ingrid Haff
On Tradeoffs in Learning-Augmented Algorithms Ziyad Benomar · Vianney Perchet
Zero-Shot Action Generalization with Limited Observations Abdullah Alchihabi · Hanping Zhang · Yuhong Guo
Noise-Aware Differentially Private Variational Inference Talal Alrawajfeh · Joonas Jälkö · Antti Honkela
Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport Jayoung Ryu · Charlotte Bunne · Luca Pinello · Aviv Regev · Romain Lopez
Optimal Stochastic Trace Estimation in Generative Modeling Xinyang Liu · Hengrong Du · Wei Deng · Ruqi Zhang
Variational Inference on the Boolean Hypercube with the Quantum Entropy Eliot Beyler · Francis Bach
Statistical Test for Auto Feature Engineering by Selective Inference Tatsuya Matsukawa · Tomohiro Shiraishi · Shuichi Nishino · Teruyuki Katsuoka · Ichiro Takeuchi
Recursive Learning of Asymptotic Variational Objectives Alessandro Mastrototaro · Mathias Müller · Jimmy Olsson
Conditional Prediction ROC Bands for Graph Classification Yujia Wu · Bo Yang · Elynn Chen · Yuzhou Chen · Zheshi Zheng
Learning from biased positive-unlabeled data via threshold calibration Paweł Teisseyre · Timo Martens · Jessa Bekker · Jesse Davis
Locally Optimal Descent for Dynamic Stepsize Scheduling Gilad Yehudai · Alon Cohen · Amit Daniely · Yoel Drori · Tomer Koren · Mariano Schain
On Local Posterior Structure in Deep Ensembles Jordahn · Jonas Vestergaard Jensen · Mikkel Schmidt · Michael Andersen
Task-Driven Discrete Representation Learning Tung Long Vuong
RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks Eduard Tulchinskii · Daria Voronkova · Ilya Trofimov · Evgeny Burnaev · Serguei Barannikov
Pareto Set Identification With Posterior Sampling Cyrille Kone · Marc Jourdan · Emilie Kaufmann
Density Ratio-based Proxy Causal Learning Without Density Ratios Bariscan Bozkurt · Ben Deaner · Dimitri Meunier · Liyuan Xu · Arthur Gretton
Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix Charles Margossian · Lawrence Saul
Riemann $^2$ : Learning Riemannian Submanifolds from Riemannian Data Leonel Rozo · Miguel González-Duque · Noémie Jaquier · Soren Hauberg
Permutation Invariant Functions: Statistical Testing, Density Estimation, and Metric Entropy Wee Chaimanowong · Ying Zhu
Density Ratio Estimation via Sampling along Generalized Geodesics on Statistical Manifolds Masanari Kimura · Howard Bondell
Efficient Estimation of a Gaussian Mean with Local Differential Privacy Nikita Kalinin · Lukas Steinberger
DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification Xiaoxue Han · Huzefa Rangwala · Yue Ning
Separation-Based Distance Measures for Causal Graphs Jonas Wahl · Jakob Runge
Noisy Low-Rank Matrix Completion via Transformed $L_1$ Regularization and its Theoretical Properties Kun Zhao · Jiayi Wang · Yifei Lou
Analyzing the Role of Permutation Invariance in Linear Mode Connectivity Keyao Zhan · Puheng Li · Lei Wu
Poisoning Bayesian Inference via Data Deletion and Replication Matthieu Carreau · Roi Naveiro · William Caballero
A Family of Distributions of Random Subsets for Controlling Positive and Negative Dependence Takahiro Kawashima · Hideitsu Hino
High-probability Convergence Bounds for Online Nonlinear Stochastic Gradient Descent under Heavy-tailed Noise Aleksandar Armacki · Shuhua Yu · Pranay Sharma · Gauri Joshi · Dragana Bajovic · Dusan Jakovetic · Soummya Kar
Kernel Single Proxy Control for Deterministic Confounding Liyuan Xu · Arthur Gretton
Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPs Kihyuk (Ki) Hong · Woojin Chae · Yufan Zhang · Dabeen Lee · Ambuj Tewari
Spectral Representation for Causal Estimation with Hidden Confounders Haotian Sun · Antoine Moulin · Tongzheng Ren · Arthur Gretton · Bo Dai
Weighted Sum of Gaussian Process Latent Variable Models James Odgers · Ruby Sedgwick · Chrysoula Kappatou · Ruth Misener · Sarah Filippi
Safety in the Face of Adversity: Achieving Zero Constraint Violation in Online Learning with Slowly Changing Constraints Bassel Hamoud · Ilnura Usmanova · Kfir Yehuda Levy
Distance Estimation for High-Dimensional Discrete Distributions Kuldeep S. Meel · Gunjan Kumar · Yash Pote
Change Point Detection in Hadamard Spaces by Alternating Minimization Anica Kostic · Vincent Runge · Charles Truong
The Hardness of Validating Observational Studies with Experimental Data Jake Fawkes · Michael O'Riordan · Athanasios Vlontzos · Oriol Corcoll · Ciarán Gilligan-Lee
Distributional Counterfactual Explanations With Optimal Transport Lei You · Lele Cao · Mattias Nilsson · Bo Zhao · Lei Lei
Heterogeneous Graph Structure Learning through the Lens of Data-generating Processes Keyue Jiang · Bohan Tang · Xiaowen Dong · Laura Toni
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting Fuqiang Liu · Sicong Jiang · Luis Miranda-Moreno · Seongjin Choi · Lijun Sun
Optimizing Neural Network Training and Quantization with Rooted Logistic Objectives Zhu Wang · Praveen Raj Veluswami · Harsh Mishra · Sathya N. Ravi
A Causal Framework for Evaluating Deferring Systems Filippo Palomba · Andrea Pugnana · Jose M. Alvarez · Salvatore Ruggieri
Asynchronous Decentralized Optimization with Constraints: Achievable Speeds of Convergence for Directed Graphs firooz shahriari-mehr · Ashkan Panahi
Score matching for bridges without learning time-reversals Baker · Moritz Schauer · Stefan Sommer
Relating Piecewise Linear Kolmogorov Arnold Networks to ReLU Networks Nandi Schoots · Mattia Jacopo Villani · Niels uit de Bos
Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees yuyang deng · Fuli Qiao · Mehrdad Mahdavi
Recurrent Neural Goodness-of-Fit Test for Time Series Aoran Zhang · Wenbin Zhou · Liyan Xie · Shixiang Zhu
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits Sreejeet Maity · Aritra Mitra
Meta-learning from Heterogeneous Tensors for Few-shot Tensor Completion Tomoharu Iwata · Atsutoshi Kumagai
On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond Dun Zeng · Zenglin Xu · SHIYU LIU · Yu Pan · Qifan Wang · Xiaoying Tang
Near-Optimal Sample Complexity in Reward-Free Kernel-based Reinforcement Learning Aya Kayal · Sattar Vakili · Laura Toni · Alberto Bernacchia
Reinforcement Learning for Adaptive MCMC Congye Wang · Wilson Ye Chen · Heishiro Kanagawa · Chris Oates
Improved dependence on coherence in eigenvector and eigenvalue estimation error bounds Hao Yan · Keith Levin
Online Assortment and Price Optimization Under Contextual Choice Models Yigit Efe Erginbas · Thomas Courtade · Ramchandran Kannan
Unbiased Quantization of the $L_1$ Ball for Communication-Efficient Distributed Mean Estimation Nithish Suresh Babu · Ritesh Kumar · Shashank Vatedka
Differentially Private Graph Data Release: Inefficiencies & Unfairness Ferdinando Fioretto · Diptangshu Sen · Juba Ziani
A Generalized Theory of Mixup for Structure-Preserving Synthetic Data Chungpa Lee · Jongho Im · Joseph Kim
The Uniformly Rotated Mondrian Kernel Calvin Osborne · Eliza O'Reilly
Q-function Decomposition with Intervention Semantics for Factored Action Spaces Junkyu Lee · Tian Gao · Elliot Nelson · Miao Liu · Debarun Bhattacharjya · Songtao Lu
Reliable and Scalable Variable Importance Estimation via Warm-start and Early Stopping Zexuan Sun · Garvesh Raskutti
Paths and Ambient Spaces in Neural Loss Landscapes Daniel Dold · Julius Kobialka · Nicolai Palm · Emanuel Sommer · David Rügamer · Oliver Dürr
Density-Dependent Group Testing Rahil Morjaria · Saikiran Bulusu · Venkata Gandikota · Sidharth Jaggi
Robust Offline Policy Learning with Observational Data from Multiple Sources Aldo Carranza · Susan Athey
Generalized Criterion for Identifiability of Additive Noise Models Using Majorization Aramayis Dallakyan · Yang Ni
Model Evaluation in the Dark: Robust Classifier Metrics with Missing Labels Danial Dervovic · Michael Cashmore
Differentiable Causal Structure Learning with Identifiability by NOTIME Jeroen Berrevoets · Jakob Raymaekers · Mihaela van der Schaar · Tim Verdonck · Ruicong Yao
No-Regret Bayesian Optimization with Stochastic Observation Failures Shogo Iwazaki · Tomohiko Tanabe · Mitsuru Irie · Shion Takeno · Kota Matsui · Yu Inatsu
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift Mitsuhiro Fujikawa · Youhei Akimoto · Jun Sakuma · Kazuto Fukuchi
The Local Learning Coefficient: A Singularity-Aware Complexity Measure Edmund Lau · Zach Furman · George Wang · Daniel Murfet · Susan Wei
All or None: Identifiable Linear Properties of Next-Token Predictors in Language Modeling Emanuele Marconato · Sebastien Lachapelle · Sebastian Weichwald · Luigi Gresele
Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data Hongni Wang · Junxi Zhang · Na Li · Linglong Kong · Bei Jiang · Xiaodong Yan
Robust Score Matching Richard Schwank · Andrew McCormack · Mathias Drton
Learning the Distribution Map in Reverse Causal Performative Prediction Daniele Bracale · Subha Maity · Yuekai Sun · Moulinath Banerjee
Computing high-dimensional optimal transport by flow neural networks Chen Xu · Xiuyuan Cheng · Yao Xie
New User Event Prediction Through the Lens of Causal Inference Henry Yuchi · Shixiang Zhu · Li Dong · Yigit Arisoy · Matthew Spencer
Neural Point Processes for Pixel-wise Regression Chengzhi Shi · Gözde Özcan · Miquel Sirera Perelló · Yuanyuan Li · Nina I. Shamsi · Stratis Ioannidis
Quantile Additive Trend Filtering Zhi Zhang · Kyle Ritscher · Oscar Madrid
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis Ruichen Luo · Sebastian Stich · Samuel Horvath · Martin Takac
Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment Qizhang Feng · Siva Rajesh Kasa · SANTHOSH KASA · Hyokun Yun · Choon Teo · Sravan Babu Bodapati
Testing Conditional Independence with Deep Neural Network Based Binary Expansion Testing (DeepBET) Yang Yang · Kai Zhang · Ping-Shou Zhong
The cost of local and global fairness in Federated Learning Yuying Duan · Gelei Xu · Yiyu Shi · Michael Lemmon
Training LLMs with MXFP4 Albert Tseng · Tao Yu · Youngsuk Park
Incremental Uncertainty-aware Performance Monitoring with Active Labeling Intervention Alexander Koebler · Thomas Decker · Ingo Thon · Volker Tresp · Florian Buettner
Consistent Validation for Predictive Methods in Spatial Settings David Burt · Yunyi Shen · Tamara Broderick
Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership Leakage Achraf Azize · Debabrota Basu
Improving N-Glycosylation and Biopharmaceutical Production Predictions Using AutoML-Built Residual Hybrid Models Pedro Seber e Silva · Richard Braatz
Order-Optimal Regret in Distributed Kernel Bandits Nikola Pavlovic · Sudeep Salgia · Qing Zhao
Beyond Size-Based Metrics: Measuring Task-Specific Complexity in Symbolic Regression Krzysztof Kacprzyk · Mihaela van der Schaar
The Polynomial Iteration Complexity for Variance Exploding Diffusion Models: Elucidating SDE and ODE Samplers Ruofeng Yang · Bo Jiang · Shuai Li
On the Consistent Recovery of Joint Distributions from Conditionals Mahbod Majid · Rattana Pukdee · Vishwajeet Agrawal · Burak Varici · Pradeep Ravikumar
Achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ Regret in Average-Reward POMDPs with Known Observation Models Alessio Russo · Alberto Maria Metelli · Marcello Restelli
Locally Private Sampling with Public Data Behnoosh Zamanlooy · Mario Diaz · Shahab Asoodeh
Wasserstein Distributionally Robust Bayesian Optimization with Continuous Context Francesco Micheli · Efe Balta · Anastasios Tsiamis · John Lygeros
Unconditionally Calibrated Priors for Beta Mixture Density Networks Alix LHERITIER · Maurizio Filippone
On Distributional Discrepancy for Experimental Design with General Assignment Probabilities Anup Rao · Peng Zhang
Axiomatic Explainer Globalness via Optimal Transport Davin Hill · Joshua Bone · Aria Masoomi · Max Torop · Jennifer Dy
Understanding the Effect of GCN Convolutions in Regression Tasks Juntong Chen · Johannes Schmidt-Hieber · Claire Donnat · Olga Klopp
Cross Validation for Correlated Data in Classification Models Oren Yuval · Saharon Rosset
Graph-based Complexity for Causal Effect by Empirical Plug-in Rina Dechter · Annie Raichev · Jin Tian · Alexander Ihler
Approximate Equivariance in Reinforcement Learning Jung Yeon Park · Sujay Bhatt · Sihan Zeng · Lawson Wong · Alec Koppel · Sumitra Ganesh · Robin Walters
Differentially Private Kernelized Contextual Bandits Nikola Pavlovic · Sudeep Salgia · Qing Zhao
Understanding GNNs and Homophily in Dynamic Node Classification Michael Ito · Danai Koutra · Jenna Wiens
Decoupling epistemic and aleatoric uncertainties with possibility theory Nong Hieu · Jeremie Houssineau · Neil Chada · Emmanuel Delande
Improving Stochastic Cubic Newton with Momentum El Mahdi Chayti · Nikita Doikov · Martin Jaggi
Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimality Ruijia Zhang · Siliang Zeng · Chenliang Li · Alfredo Garcia · Mingyi Hong
Factor Analysis with Correlated Topic Model for Multi-Modal Data Małgorzata Łazęcka · Ewa Szczurek
Decision-Point Guided Safe Policy Improvement Abhishek Sharma · Leo Benac · Sonali Parbhoo · Finale Doshi-Velez
Mixed-Feature Logistic Regression Robust to Distribution Shifts Qingshi Sun · Nathan Justin · Andres Gomez · Phebe Vayanos
Scalable Implicit Graphon Learning Ali Azizpour · Nicolas Zilberstein · Santiago Segarra
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces Imad Aouali · Victor-Emmanuel Brunel · David Rohde · Anna Korba
Additive Model Boosting: New Insights and Path(ologie)s Rickmer Schulte · David Rügamer
Clustered Invariant Risk Minimization Tomoya Murata · Atsushi Nitanda · Taiji Suzuki
Analysis of Two-Stage Rollout Designs with Clustering for Causal Inference under Network Interference Mayleen Cortez-Rodriguez · Matthew Eichhorn · Christina Yu
A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries Ashkan Soleymani · Behrooz Tahmasebi · Stefanie Jegelka · Patrick Jaillet
Global Group Fairness in Federated Learning via Function Tracking Yves Rychener · Daniel Kuhn · Yifan Hu
TRADE: Transfer of Distributions between External Conditions with Normalizing Flows Stefan Wahl · Armand Rousselot · Felix Draxler · Ullrich Köthe