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ORCID27th AISTATS 2024: Valencia, Spain
- Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li:
International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain. Proceedings of Machine Learning Research 238, PMLR 2024 - David Rügamer:
Scalable Higher-Order Tensor Product Spline Models. 1-9 - Daichi Amagata:
Fair k-center Clustering with Outliers. 10-18 - Shiv Shankar, Ritwik Sinha, Yash Chandak, Saayan Mitra, Madalina Fiterau:
A/B testing under Interference with Partial Network Information. 19-27 - Taeuk Jang, Hongchang Gao, Pengyi Shi, Xiaoqian Wang:
Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning. 28-36 - Wenjie Li, Qifan Song, Jean Honorio:
Personalized Federated X-armed Bandit. 37-45 - Zhishuai Li, Yunhao Nie, Ziyue Li, Lei Bai, Yisheng Lv, Rui Zhao:
Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning. 46-54 - Davin Hill, Aria Masoomi, Max Torop, Sandesh Ghimire, Jennifer G. Dy:
Boundary-Aware Uncertainty for Feature Attribution Explainers. 55-63 - Mathieu Even, Anastasia Koloskova, Laurent Massoulié:
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. 64-72 - Fredrik Hellström, Benjamin Guedj:
Comparing Comparators in Generalization Bounds. 73-81 - Mathieu Dagréou, Thomas Moreau, Samuel Vaiter, Pierre Ablin:
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. 82-90 - Vincent Zhihao Zheng, Seongjin Choi, Lijun Sun:
Better Batch for Deep Probabilistic Time Series Forecasting. 91-99 - Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Yifan Hu, Andreas Krause, Ilija Bogunovic:
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces. 100-108 - Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue, Florence d'Alché-Buc:
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels. 109-117 - Nelson Vadori, Rahul Savani:
Ordinal Potential-based Player Rating. 118-126 - Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe:
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. 127-135 - Amanda M. Buch, Conor Liston, Logan Grosenick:
Simple and scalable algorithms for cluster-aware precision medicine. 136-144 - Tianyi Lin, Marco Cuturi, Michael I. Jordan:
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport. 145-153 - Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang:
Local Causal Discovery with Linear non-Gaussian Cyclic Models. 154-162 - Yookoon Park, David M. Blei:
Density Uncertainty Layers for Reliable Uncertainty Estimation. 163-171 - Florence Carton, Robin Louiset, Pietro Gori:
Double InfoGAN for Contrastive Analysis. 172-180 - Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner:
Is this model reliable for everyone? Testing for strong calibration. 181-189 - Nicolai Palm, Thomas Nagler:
An Online Bootstrap for Time Series. 190-198 - Yue Xing, Xiaofeng Lin, Qifan Song, Yi Xu, Belinda Zeng, Guang Cheng:
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective. 199-207 - Zhao Song, Junze Yin, Lichen Zhang:
Solving Attention Kernel Regression Problem via Pre-conditioner. 208-216 - Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao:
Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations. 217-225 - Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier:
Identifying Copeland Winners in Dueling Bandits with Indifferences. 226-234 - Kyurae Kim, Yi-An Ma, Jacob R. Gardner:
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? 235-243 - Zhao Song, Junze Yin, Lichen Zhang, Ruizhe Zhang:
Fast Dynamic Sampling for Determinantal Point Processes. 244-252 - Zitian Li, Wang Chi Cheung:
Best Arm Identification with Resource Constraints. 253-261 - Haoming Yang, Ali Hasan, Yuting Ng, Vahid Tarokh:
Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes. 262-270 - Zhenyu Zhang, JiuDong Yang:
HintMiner: Automatic Question Hints Mining From Q&A Web Posts with Language Model via Self-Supervised Learning. 271-279 - Kihyuk Hong, Yuhang Li, Ambuj Tewari:
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning. 280-288 - Jiawei Huang, Batuhan Yardim, Niao He:
On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation. 289-297 - Pinar Demetci, Quang Huy Tran, Ievgen Redko, Ritambhara Singh:
Breaking isometric ties and introducing priors in Gromov-Wasserstein distances. 298-306 - Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen:
Enhancing In-context Learning via Linear Probe Calibration. 307-315 - Meixia Lin, Yangjing Zhang:
DNNLasso: Scalable Graph Learning for Matrix-Variate Data. 316-324 - Guillaume Houry, Han Bao, Han Zhao, Makoto Yamada:
Fast 1-Wasserstein distance approximations using greedy strategies. 325-333 - Emil Carlsson, Debabrota Basu, Fredrik D. Johansson, Devdatt P. Dubhashi:
Pure Exploration in Bandits with Linear Constraints. 334-342 - Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel:
Emergent specialization from participation dynamics and multi-learner retraining. 343-351 - Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin:
Optimal Sparse Survival Trees. 352-360 - Chen Li, Yoshihiro Yamanishi:
TenGAN: Pure Transformer Encoders Make an Efficient Discrete GAN for De Novo Molecular Generation. 361-369 - Yuya Yoshikawa, Tomoharu Iwata:
Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizers. 370-378 - Dorian Baudry, Nadav Merlis, Mathieu Benjamin Molina, Hugo Richard, Vianney Perchet:
Multi-armed bandits with guaranteed revenue per arm. 379-387 - Hugo Richard, Etienne Boursier, Vianney Perchet:
Constant or Logarithmic Regret in Asynchronous Multiplayer Bandits with Limited Communication. 388-396 - Rafael Savvides, Hoang Phuc Hau Luu, Kai Puolamäki:
Error bounds for any regression model using Gaussian processes with gradient information. 397-405 - Felip Guimerà Cuevas, Helmut Schmid:
Robust Non-linear Normalization of Heterogeneous Feature Distributions with Adaptive Tanh-Estimators. 406-414 - Dongxia Wu, Tsuyoshi Idé, Georgios Kollias, Jirí Navrátil, Aurélie C. Lozano, Naoki Abe, Yi-An Ma, Rose Yu:
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. 415-423 - Andrew R. Hands, Tianyi Sun, Risi Kondor:
P-tensors: a General Framework for Higher Order Message Passing in Subgraph Neural Networks. 424-432 - Aadirupa Saha, Vitaly Feldman, Yishay Mansour, Tomer Koren:
Faster Convergence with MultiWay Preferences. 433-441 - Yanxiang Gong, Zhiwei Xie, Mei Xie, Xin Ma:
Testing Generated Distributions in GANs to Penalize Mode Collapse. 442-450 - Vivien A Cabannnes, Francis Bach:
The Galerkin method beats Graph-Based Approaches for Spectral Algorithms. 451-459 - Jin Sima, Changlong Wu, Olgica Milenkovic, Wojciech Szpankowski:
Online Distribution Learning with Local Privacy Constraints. 460-468 - Hyeok Kyu Kwon, Minwoo Chae:
Minimax optimal density estimation using a shallow generative model with a one-dimensional latent variable. 469-477 - Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab:
Delegating Data Collection in Decentralized Machine Learning. 478-486 - Berivan Isik, Francesco Pase, Deniz Gündüz, Sanmi Koyejo, Tsachy Weissman, Michele Zorzi:
Adaptive Compression in Federated Learning via Side Information. 487-495 - Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Xingchen Wan, Vu Nguyen, Harald Oberhauser, Michael A. Osborne:
Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach. 496-504 - Masaki Adachi, Brady Planden, David A. Howey, Michael A. Osborne, Sebastian Orbell, Natalia Ares, Krikamol Muandet, Siu Lun Chau:
Looping in the Human: Collaborative and Explainable Bayesian Optimization. 505-513 - Sagnik Chatterjee, SAPV Tharrmashastha, Debajyoti Bera:
Efficient Quantum Agnostic Improper Learning of Decision Trees. 514-522 - Steven Bilaj, Sofien Dhouib, Setareh Maghsudi:
Meta Learning in Bandits within shared affine Subspaces. 523-531 - Guillaume Braun, Masashi Sugiyama:
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates. 532-540 - Jin Zhu, Runzhe Wan, Zhengling Qi, Shikai Luo, Chengchun Shi:
Robust Offline Reinforcement Learning with Heavy-Tailed Rewards. 541-549 - Hidde Fokkema, Damien Garreau, Tim van Erven:
The Risks of Recourse in Binary Classification. 550-558 - Yingru Li, Zhi-Quan Luo:
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation. 559-567 - Mina Dalirrooyfard, Elaheh Fata, Majid Behbahani, Yuriy Nevmyvaka:
Graph Partitioning with a Move Budget. 568-576 - Myrto Limnios, Stéphan Clémençon:
On Ranking-based Tests of Independence. 577-585 - Othmane Sebbouh, Marco Cuturi, Gabriel Peyré:
Structured Transforms Across Spaces with Cost-Regularized Optimal Transport. 586-594 - Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko:
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias. 595-603 - Shubham Gupta, Peter W. J. Staar, Christian de Sainte Marie:
Clustering Items From Adaptively Collected Inconsistent Feedback. 604-612 - Mahmoud Hegazy, Rémi Leluc, Cheuk Ting Li, Aymeric Dieuleveut:
Compression with Exact Error Distribution for Federated Learning. 613-621 - Teodora Pandeva, Patrick Forré, Aaditya Ramdas, Shubhanshu Shekhar:
Deep anytime-valid hypothesis testing. 622-630 - Ethan Blaser, Chuanhao Li, Hongning Wang:
Federated Linear Contextual Bandits with Heterogeneous Clients. 631-639 - Phi Vu Tran:
LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object Detection. 640-648 - Rustem Islamov, Mher Safaryan, Dan Alistarh:
AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms. 649-657 - Spencer Hutchinson
, Berkay Turan, Mahnoosh Alizadeh:
Directional Optimism for Safe Linear Bandits. 658-666 - Zijun Cui, Hanjing Wang, Tian Gao, Kartik Talamadupula, Qiang Ji:
Theory-guided Message Passing Neural Network for Probabilistic Inference. 667-675 - Zhenyu Sun, Xiaochun Niu, Ermin Wei:
Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters. 676-684 - Yingcong Li, Yixiao Huang, Muhammed Emrullah Ildiz, Ankit Singh Rawat, Samet Oymak:
Mechanics of Next Token Prediction with Self-Attention. 685-693 - Siqi Zhang, Yifan Hu, Liang Zhang, Niao He:
Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization. 694-702 - Zelin He, Ying Sun, Runze Li:
TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional Regression. 703-711 - Daiqi Gao, Yuanjia Wang, Donglin Zeng:
Fusing Individualized Treatment Rules Using Secondary Outcomes. 712-720 - David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvári:
Exploration via linearly perturbed loss minimisation. 721-729 - Jizhou Liu, Eric Tchetgen Tchetgen, Carlos Varjão:
Proximal Causal Inference for Synthetic Control with Surrogates. 730-738 - Martin Jankowiak, Du Phan:
Reparameterized Variational Rejection Sampling. 739-747 - Thuan Anh Trang, Nhat Khang Ngo, Daniel T. Levy, Ngoc Thieu Vo, Siamak Ravanbakhsh, Truong Son Hy:
E(3)-Equivariant Mesh Neural Networks. 748-756 - Lianke Qin, Zhao Song, Ruizhe Zhang:
A General Algorithm for Solving Rank-one Matrix Sensing. 757-765 - Lequn Wang, Akshay Krishnamurthy, Alex Slivkins:
Oracle-Efficient Pessimism: Offline Policy Optimization In Contextual Bandits. 766-774 - Xavier Dupuis, Patrick Tardivel:
The Solution Path of SLOPE. 775-783 - He Chen, Haochen Xu, Rujun Jiang, Anthony Man-Cho So:
Lower-level Duality Based Reformulation and Majorization Minimization Algorithm for Hyperparameter Optimization. 784-792 - Pavan Karjol, Rohan Kashyap, Aditya Gopalan, A. P. Prathosh:
A Unified Framework for Discovering Discrete Symmetries. 793-801 - Chen Amiraz, Robert Krauthgamer, Boaz Nadler:
Recovery Guarantees for Distributed-OMP. 802-810 - Matteo Vilucchio, Emanuele Troiani, Vittorio Erba, Florent Krzakala:
Asymptotic Characterisation of the Performance of Robust Linear Regression in the Presence of Outliers. 811-819 - Hanlin Yu, Marcelo Hartmann, Bernardo Williams Moreno Sanchez, Mark Girolami, Arto Klami:
Riemannian Laplace Approximation with the Fisher Metric. 820-828 - Tim Reichelt, Luke Ong, Tom Rainforth:
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support. 829-837 - Anass Aghbalou, Anne Sabourin, François Portier:
Sharp error bounds for imbalanced classification: how many examples in the minority class? 838-846 - Freddie Bickford Smith, Adam Foster, Tom Rainforth:
Making Better Use of Unlabelled Data in Bayesian Active Learning. 847-855 - Nikita Puchkin, Eduard Gorbunov, Nikolay Kutuzov, Alexander V. Gasnikov:
Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems. 856-864 - Kartik Ahuja, Amin Mansouri, Yixin Wang:
Multi-Domain Causal Representation Learning via Weak Distributional Invariances. 865-873 - Amirhossein Ahmadian, Yifan Ding, Gabriel Eilertsen, Fredrik Lindsten:
Unsupervised Novelty Detection in Pretrained Representation Space with Locally Adapted Likelihood Ratio. 874-882 - Damien Scieur:
Adaptive Quasi-Newton and Anderson Acceleration Framework with Explicit Global (Accelerated) Convergence Rates. 883-891 - Wenxuan Bao, Jun Wu, Jingrui He:
BOBA: Byzantine-Robust Federated Learning with Label Skewness. 892-900 - Zhongliang Guo, Weiye Li, Yifei Qian, Ognjen Arandjelovic, Lei Fang:
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models. 901-909 - Florence Regol, Mark Coates:
Categorical Generative Model Evaluation via Synthetic Distribution Coarsening. 910-918 - Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio:
Monitoring machine learning-based risk prediction algorithms in the presence of performativity. 919-927 - Adela Frances DePavia, Erasmo Tani, Ali Vakilian:
Learning-Based Algorithms for Graph Searching Problems. 928-936 - Francesco Bacchiocchi, Gianmarco Genalti, Davide Maran, Marco Mussi, Marcello Restelli, Nicola Gatti, Alberto Maria Metelli:
Autoregressive Bandits. 937-945 - Taehyo Kim, Hai Shu, Qiran Jia, Mony de Leon:
DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data. 946-954 - Zhenzhang Ye, Gabriel Peyré, Daniel Cremers, Pierre Ablin:
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization. 955-963 - Lena Stempfle, Fredrik D. Johansson:
MINTY: Rule-based models that minimize the need for imputing features with missing values. 964-972 - Itamar Zimerman, Lior Wolf:
Multi-Dimensional Hyena for Spatial Inductive Bias. 973-981 - Amber Yijia Zheng, Tong He, Yixuan Qiu, Minjie Wang, David Wipf:
Graph Machine Learning through the Lens of Bilevel Optimization. 982-990 - Youssef Allouah, Rachid Guerraoui, Lê-Nguyên Hoang, Oscar Villemaud:
Robust Sparse Voting. 991-999 - Siddharth Joshi, Arnav Jain, Ali Payani, Baharan Mirzasoleiman:
Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity. 1000-1008 - Soo Min Kwon, Zekai Zhang, Dogyoon Song, Laura Balzano, Qing Qu:
Efficient Low-Dimensional Compression of Overparameterized Models. 1009-1017 - Mohan Wu, Martin Lysy:
Data-Adaptive Probabilistic Likelihood Approximation for Ordinary Differential Equations. 1018-1026 - Marwa El Halabi, Jakub Tarnawski, Ashkan Norouzi-Fard, Thuy-Duong Vuong:
Fairness in Submodular Maximization over a Matroid Constraint. 1027-1035 - Shuo Shuo Liu:
Unified Transfer Learning in High-Dimensional Linear Regression. 1036-1044 - Piersilvio De Bartolomeis, Javier Abad Martinez, Konstantin Donhauser, Fanny Yang:
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials. 1045-1053 - Arnob Ghosh, Xingyu Zhou, Ness B. Shroff:
Towards Achieving Sub-linear Regret and Hard Constraint Violation in Model-free RL. 1054-1062 - Liyan Xie, Yuchen Liang, Venugopal V. Veeravalli:
Distributionally Robust Quickest Change Detection using Wasserstein Uncertainty Sets. 1063-1071 - Sree Harsha Tanneru, Chirag Agarwal, Himabindu Lakkaraju:
Quantifying Uncertainty in Natural Language Explanations of Large Language Models. 1072-1080 - Loay Raed Mualem, Ethan R. Elenberg, Moran Feldman, Amin Karbasi:
Submodular Minimax Optimization: Finding Effective Sets. 1081-1089 - Rajdeep Haldar, Yue Xing, Qifan Song:
Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability. 1090-1098 - Futoshi Futami, Tomoharu Iwata:
Information-theoretic Analysis of Bayesian Test Data Sensitivity. 1099-1107 - Ron Mosenzon, Ali Vakilian:
Scalable Algorithms for Individual Preference Stable Clustering. 1108-1116 - Fan Yang, Pierre Le Bodic, Michael Kamp, Mario Boley:
Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles. 1117-1125 - Xiaocheng Li, Shang Liu, Chunlin Sun, Hanzhao Wang:
When No-Rejection Learning is Consistent for Regression with Rejection. 1126-1134 - Soheun Yi, Sanghack Lee:
Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models. 1135-1143 - Matthew J. Holland:
Robust variance-regularized risk minimization with concomitant scaling. 1144-1152 - Yajing Fan, Wanli Shi, Yi Chang, Bin Gu:
Fast and Adversarial Robust Kernelized SDU Learning. 1153-1161 - Zhou Zhai, Wanli Shi, Heng Huang, Yi Chang, Bin Gu:
Learning Sampling Policy to Achieve Fewer Queries for Zeroth-Order Optimization. 1162-1170 - Yakov Medvedovsky, Eran Treister, Tirza S. Routtenberg:
Efficient Graph Laplacian Estimation by Proximal Newton. 1171-1179 - Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar:
Adaptive Experiment Design with Synthetic Controls. 1180-1188 - Alejandro D. de la Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos:
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization. 1189-1197 - Ainhize Barrainkua, Paula Gordaliza, José Antonio Lozano, Novi Quadrianto:
Uncertainty Matters: Stable Conclusions under Unstable Assessment of Fairness Results. 1198-1206 - Ahmad Rammal, Kaja Gruntkowska, Nikita Fedin, Eduard Gorbunov, Peter Richtárik:
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates. 1207-1215 - Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicolò Cesa-Bianchi:
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits. 1216-1224 - Shintaro Nakamura, Masashi Sugiyama:
Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. 1225-1233 - Susanne Frick, Amer Krivosija, Alexander Munteanu:
Scalable Learning of Item Response Theory Models. 1234-1242 - Andi Nika, Debmalya Mandal, Adish Singla, Goran Radanovic:
Corruption-Robust Offline Two-Player Zero-Sum Markov Games. 1243-1251 - Shogo Iwazaki, Tomohiko Tanabe, Mitsuru Irie, Shion Takeno, Yu Inatsu:
Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum. 1252-1260 - Frederiek Wesel, Kim Batselier:
Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models. 1261-1269 - Rune D. Kjærsgaard, Pekka Parviainen, Saket Saurabh, Madhumita Kundu, Line H. Clemmensen:
Fair Soft Clustering. 1270-1278 - Alexander Tong, Nikolay Malkin, Kilian Fatras, Lazar Atanackovic, Yanlei Zhang, Guillaume Huguet, Guy Wolf, Yoshua Bengio:
Simulation-Free Schrödinger Bridges via Score and Flow Matching. 1279-1287 - Alexia Jolicoeur-Martineau, Kilian Fatras, Tal Kachman:
Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees. 1288-1296 - Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier, Brian Staber:
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels. 1297-1305 - Daniel Robert-Nicoud, Andreas Krause, Viacheslav Borovitskiy:
Intrinsic Gaussian Vector Fields on Manifolds. 1306-1314 - Lucas Cosier, Rares Iordan, Sicelukwanda N. T. Zwane, Giovanni Franzese, James T. Wilson, Marc Peter Deisenroth, Alexander Terenin, Yasemin Bekiroglu:
A Unifying Variational Framework for Gaussian Process Motion Planning. 1315-1323 - Clément Bénard, Jeffrey Näf, Julie Josse:
MMD-based Variable Importance for Distributional Random Forest. 1324-1332 - Jörn Tebbe, Christoph Zimmer, Ansgar Steland, Markus Lange-Hegermann, Fabian Mies:
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning. 1333-1341 - Soheila Molaei, Anshul Thakur, Ghazaleh Niknam, Andrew A. S. Soltan, Hadi Zare, David A. Clifton:
Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks. 1342-1350 - Jimmy Hickey, Ricardo Henao, Daniel Wojdyla, Michael J. Pencina, Matthew Engelhard:
Adaptive Discretization for Event PredicTion (ADEPT). 1351-1359 - Jung Eun Huh, Patrick Rebeschini:
Generalization Bounds for Label Noise Stochastic Gradient Descent. 1360-1368 - Marzi Heidari, Abdullah Alchihabi, Qing En, Yuhong Guo:
Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification. 1369-1377 - Zulqarnain Khan, Davin Hill, Aria Masoomi, Joshua T. Bone, Jennifer G. Dy:
Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions. 1378-1386 - Buu Phan, Ashish Khisti, Christos Louizos:
Importance Matching Lemma for Lossy Compression with Side Information. 1387-1395 - Konstantin Donhauser, Johan Lokna, Amartya Sanyal, March Boedihardjo, Robert Hönig, Fanny Yang:
Certified private data release for sparse Lipschitz functions. 1396-1404 - Jacob Trauger, Ambuj Tewari:
Sequence Length Independent Norm-Based Generalization Bounds for Transformers. 1405-1413 - Kexin Jin, Chenguang Liu, Jonas Latz:
Subsampling Error in Stochastic Gradient Langevin Diffusions. 1414-1422 - Minh N. Vu, Truc D. T. Nguyen, Tre' R. Jeter, My T. Thai:
Analysis of Privacy Leakage in Federated Large Language Models. 1423-1431 - Chendi Qian, Didier Chételat, Christopher Morris:
Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems. 1432-1440 - Qing En, Yuhong Guo:
Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation. 1441-1449 - Shachi Deshpande, Charles Marx, Volodymyr Kuleshov:
Online Calibrated and Conformal Prediction Improves Bayesian Optimization. 1450-1458 - Chinmaya Kausik, Yangyi Lu, Kevin Tan, Maggie Makar, Yixin Wang, Ambuj Tewari:
Offline Policy Evaluation and Optimization Under Confounding. 1459-1467 - Bitya Neuhof, Yuval Benjamini:
Confident Feature Ranking. 1468-1476 - Jie Hu, Vishwaraj Doshi, Do Young Eun:
Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications. 1477-1485 - Harit Vishwakarma, Heguang Lin, Ramya Korlakai Vinayak:
Taming False Positives in Out-of-Distribution Detection with Human Feedback. 1486-1494 - Jonathan Lebensold, Doina Precup, Borja Balle:
On the Privacy of Selection Mechanisms with Gaussian Noise. 1495-1503 - Ulysse Gazin, Gilles Blanchard, Étienne Roquain:
Transductive conformal inference with adaptive scores. 1504-1512 - Hedda Cohen Indelman, Tamir Hazan:
Learning Latent Partial Matchings with Gumbel-IPF Networks. 1513-1521 - Serena Lutong Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan:
On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry. 1522-1530 - Christoph Dann, Claudio Gentile, Aldo Pacchiano:
Data-Driven Online Model Selection With Regret Guarantees. 1531-1539 - Raphael Rossellini, Rina Foygel Barber, Rebecca Willett:
Integrating Uncertainty Awareness into Conformalized Quantile Regression. 1540-1548 - Guneet S. Dhillon, George Deligiannidis, Tom Rainforth:
On the Expected Size of Conformal Prediction Sets. 1549-1557 - Martin Sevilla, Antonio G. Marques, Santiago Segarra:
Estimation of partially known Gaussian graphical models with score-based structural priors. 1558-1566 - Sho Takemori, Yuhei Umeda, Aditya Gopalan:
Model-Based Best Arm Identification for Decreasing Bandits. 1567-1575 - Tingting Ou, Rachel Cummings, Marco Avella Medina:
Thompson Sampling Itself is Differentially Private. 1576-1584 - Zhihan Xiong, Romain Camilleri, Maryam Fazel, Lalit Jain, Kevin G. Jamieson:
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity. 1585-1593 - Jinwon Sohn, Qifan Song, Guang Lin:
Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes. 1594-1602 - Ziye Ma, Ying Chen, Javad Lavaei, Somayeh Sojoudi:
Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses. 1603-1611 - Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi:
FedFisher: Leveraging Fisher Information for One-Shot Federated Learning. 1612-1620 - Davin Choo, Kirankumar Shiragur, Caroline Uhler:
Causal Discovery under Off-Target Interventions. 1621-1629 - Ying Jin, Ramki Gummadi, Zhengyuan Zhou, Jose H. Blanchet:
Feasible Q-Learning for Average Reward Reinforcement Learning. 1630-1638 - Xi Wang, Tomas Geffner, Justin Domke:
Joint control variate for faster black-box variational inference. 1639-1647 - Rong Tang, Yun Yang:
Adaptivity of Diffusion Models to Manifold Structures. 1648-1656 - Nathaniel Diamant, Ehsan Hajiramezanali, Tommaso Biancalani, Gabriele Scalia:
Conformalized Deep Splines for Optimal and Efficient Prediction Sets. 1657-1665 - Yasushi Esaki, Akihiro Nakamura, Keisuke Kawano, Ryoko Tokuhisa, Takuro Kutsuna:
Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex. 1666-1674 - Zeqi Ye, Hansheng Jiang:
Smoothness-Adaptive Dynamic Pricing with Nonparametric Demand Learning. 1675-1683 - Zhaoqi Li, Kevin G. Jamieson, Lalit Jain:
Optimal Exploration is no harder than Thompson Sampling. 1684-1692 - Junze Deng, Yuan Cheng, Shaofeng Zou, Yingbin Liang:
Sample Complexity Characterization for Linear Contextual MDPs. 1693-1701 - Soumyabrata Pal, Prateek Varshney, Gagan Madan, Prateek Jain, Abhradeep Thakurta, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava:
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components. 1702-1710 - Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines:
Queuing dynamics of asynchronous Federated Learning. 1711-1719 - Gokcan Tatli, Yi Chen, Ramya Korlakai Vinayak:
Learning Populations of Preferences via Pairwise Comparison Queries. 1720-1728 - Xunzhi Xiang, Kun Jing, Jungang Xu:
A Neural Architecture Predictor based on GNN-Enhanced Transformer. 1729-1737 - Yue Liu, Ziyi Yu, Zitu Liu, Wenjie Tian:
Efficient Neural Architecture Design via Capturing Architecture-Performance Joint Distribution. 1738-1746 - Chungpa Lee, Joonhwan Chang, Jy-yong Sohn:
Analysis of Using Sigmoid Loss for Contrastive Learning. 1747-1755 - Tong Wu:
Robust Data Clustering with Outliers via Transformed Tensor Low-Rank Representation. 1756-1764 - Sangil Han, Sungkyu Jung, Kyoowon Kim:
Robust SVD Made Easy: A fast and reliable algorithm for large-scale data analysis. 1765-1773 - Hao Liang, Zhiquan Luo:
Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures. 1774-1782 - Anton Frederik Thielmann, René-Marcel Kruse, Thomas Kneib, Benjamin Säfken:
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean. 1783-1791 - Moshe Eliasof, Eldad Haber, Eran Treister, Carola-Bibiane Schönlieb:
On The Temporal Domain of Differential Equation Inspired Graph Neural Networks. 1792-1800 - Dominik Wagner, Basim Khajwal, Luke Ong:
Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing. 1801-1809 - Louis Sharrock, Daniel Dodd, Christopher Nemeth:
Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting. 1810-1818 - Daniel Dold, David Rügamer, Beate Sick, Oliver Dürr:
Bayesian Semi-structured Subspace Inference. 1819-1827 - Vo Nguyen Le Duy, Hsuan-Tien Lin, Ichiro Takeuchi:
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference. 1828-1836 - Sophie Jaffard, Samuel Vaiter, Alexandre Muzy, Patricia Reynaud-Bouret:
Provable local learning rule by expert aggregation for a Hawkes network. 1837-1845 - Juliette Achddou, Nicolò Cesa-Bianchi, Pierre Laforgue:
Multitask Online Learning: Listen to the Neighborhood Buzz. 1846-1854 - Tuukka Korhonen, Fedor V. Fomin, Pekka Parviainen:
Structural perspective on constraint-based learning of Markov networks. 1855-1863 - Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
DAGnosis: Localized Identification of Data Inconsistencies using Structures. 1864-1872 - Isabel Haasler, Pascal Frossard:
Bures-Wasserstein Means of Graphs. 1873-1881 - Nikolaos Nakis, Abdulkadir Çelikkanat, Louis Boucherie, Sune Lehmann, Morten Mørup:
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model. 1882-1890 - Marcus A. K. September, Francesco Sanna Passino, Leonie Goldmann, Anton Hinel:
Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks. 1891-1899 - Wei Zhang, Zhenni Wang:
Restricted Isometry Property of Rank-One Measurements with Random Unit-Modulus Vectors. 1900-1908 - Prakhar Verma, Vincent Adam, Arno Solin:
Variational Gaussian Process Diffusion Processes. 1909-1917 - Pan Zhao, Antoine Chambaz, Julie Josse, Shu Yang:
Positivity-free Policy Learning with Observational Data. 1918-1926 - Lars Lorch, Andreas Krause, Bernhard Schölkopf:
Causal Modeling with Stationary Diffusions. 1927-1935 - Yuma Ichikawa, Koji Hukushima:
Learning Dynamics in Linear VAE: Posterior Collapse Threshold, Superfluous Latent Space Pitfalls, and Speedup with KL Annealing. 1936-1944 - Holger S. G. Heidrich, Jannik Irmai, Bjoern Andres:
A 4-Approximation Algorithm for Min Max Correlation Clustering. 1945-1953 - Weichen Li, Rati Devidze, Waleed Mustafa, Sophie Fellenz:
Ethics in Action: Training Reinforcement Learning Agents for Moral Decision-making In Text-based Adventure Games. 1954-1962 - Stephan Wäldchen, Kartikey Sharma, Berkant Turan, Max Zimmer, Sebastian Pokutta:
Interpretability Guarantees with Merlin-Arthur Classifiers. 1963-1971 - Eugene Berta, Francis R. Bach, Michael I. Jordan:
Classifier Calibration with ROC-Regularized Isotonic Regression. 1972-1980 - Petru Tighineanu, Lukas Grossberger, Paul Baireuther, Kathrin Skubch, Stefan Falkner, Julia Vinogradska, Felix Berkenkamp:
Scalable Meta-Learning with Gaussian Processes. 1981-1989 - Lesi Chen, Haishan Ye, Luo Luo:
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. 1990-1998 - Marco Pegoraro, Sanketh Vedula, Aviv Rosenberg, Irene Tallini, Emanuele Rodolà, Alex M. Bronstein:
Vector Quantile Regression on Manifolds. 1999-2007 - Jiulin Wang, Xu Shi, Rujun Jiang:
Near-Optimal Convex Simple Bilevel Optimization with a Bisection Method. 2008-2016 - Gabriel Laberge, Yann Batiste Pequignot, Mario Marchand, Foutse Khomh:
Tackling the XAI Disagreement Problem with Regional Explanations. 2017-2025 - José Manuel de Frutos, Pablo M. Olmos, Manuel Alberto Vazquez Lopez, Joaquín Míguez:
Training Implicit Generative Models via an Invariant Statistical Loss. 2026-2034 - Junyi Fan, Yuxuan Han, Jialin Zeng, Jian-Feng Cai, Yang Wang, Yang Xiang, Jiheng Zhang:
RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model. 2035-2043 - Jing Dong, Baoxiang Wang, Yaoliang Yu:
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. 2044-2052 - Ethan B. Andrew, David R. Westhead, Luisa Cutillo:
GmGM: a fast multi-axis Gaussian graphical model. 2053-2061 - Cheuk Ting Li, Jingwei Zhang, Farzan Farnia:
On Convergence in Wasserstein Distance and f-divergence Minimization Problems. 2062-2070 - Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin:
Sparse and Faithful Explanations Without Sparse Models. 2071-2079 - Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D. Abernethy, Molei Tao:
Extragradient Type Methods for Riemannian Variational Inequality Problems. 2080-2088 - Dmytro Velychko, Simon Damm, Asja Fischer, Jörg Lücke:
Learning Sparse Codes with Entropy-Based ELBOs. 2089-2097 - Shiliang Zuo:
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses. 2098-2106 - Lorenzo Mauri, Giacomo Zanella:
Robust Approximate Sampling via Stochastic Gradient Barker Dynamics. 2107-2115 - Haoyue Tang, Tian Xie, Aosong Feng, Hanyu Wang, Chenyang Zhang, Yang Bai:
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint. 2116-2124 - Jiashuo Liu, Jiayun Wu, Jie Peng, Xiaoyu Wu, Yang Zheng, Bo Li, Peng Cui:
Enhancing Distributional Stability among Sub-populations. 2125-2133 - Harsh Parikh, Quinn Lanners, Zade Akras, Sahar Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky:
Safe and Interpretable Estimation of Optimal Treatment Regimes. 2134-2142 - Gennaro Gala, Cassio P. de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur:
Probabilistic Integral Circuits. 2143-2151 - Martino Bernasconi, Alberto Marchesi, Francesco Trovò:
Learning Extensive-Form Perfect Equilibria in Two-Player Zero-Sum Sequential Games. 2152-2160 - Rafal Szlendak, Elnur Gasanov, Peter Richtárik:
Understanding Progressive Training Through the Framework of Randomized Coordinate Descent. 2161-2169 - Mingyuan Zhang, Shivani Agarwal:
Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures. 2170-2178 - Cai Zhou, Rose Yu, Yusu Wang:
On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers. 2179-2187 - Dominik Janzing, Patrick Blöbaum, Atalanti-Anastasia Mastakouri, Philipp Michael Faller, Lenon Minorics, Kailash Budhathoki:
Quantifying intrinsic causal contributions via structure preserving interventions. 2188-2196 - Felix Draxler, Peter Sorrenson, Lea Zimmermann, Armand Rousselot, Ullrich Köthe:
Free-form Flows: Make Any Architecture a Normalizing Flow. 2197-2205 - Bianca Marin Moreno, Margaux Brégère, Pierre Gaillard, Nadia Oudjane:
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent. 2206-2214 - Yongyi Guo, Ziping Xu, Susan A. Murphy:
Online learning in bandits with predicted context. 2215-2223 - Jonathan So, Richard E. Turner:
Optimising Distributions with Natural Gradient Surrogates. 2224-2232 - Justin M. Baker, Qingsong Wang, Martin Berzins, Thomas Strohmer, Bao Wang:
Monotone Operator Theory-Inspired Message Passing for Learning Long-Range Interaction on Graphs. 2233-2241 - Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin M. Stangl:
Agnostic Multi-Robust Learning using ERM. 2242-2250 - Tolga Dimlioglu, Anna Choromanska:
GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models. 2251-2259 - Pratik Patil, Yuchen Wu, Ryan J. Tibshirani:
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent. 2260-2268 - Mahed Abroshan, Andrew Elliott, Mohammad Mahdi Khalili:
Imposing Fairness Constraints in Synthetic Data Generation. 2269-2277 - Krzysztof Choromanski, Shanda Li, Valerii Likhosherstov, Kumar Avinava Dubey, Shengjie Luo, Di He, Yiming Yang, Tamás Sarlós, Thomas Weingarten, Adrian Weller:
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers. 2278-2286 - Yun-Peng Li, Hans-Andrea Loeliger:
Backward Filtering Forward Deciding in Linear Non-Gaussian State Space Models. 2287-2295 - Nguyen Hoang Khoi Do, Tanmoy Chowdhury, Chen Ling, Liang Zhao, My T. Thai:
MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization. 2296-2304 - Wonyoung Kim, Garud Iyengar, Assaf Zeevi:
A Doubly Robust Approach to Sparse Reinforcement Learning. 2305-2313 - Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer:
General Identifiability and Achievability for Causal Representation Learning. 2314-2322 - Stephen U. Pasteris, Alberto Rumi, Fabio Vitale, Nicolò Cesa-Bianchi:
Sum-max Submodular Bandits. 2323-2331 - Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner:
Stochastic Approximation with Biased MCMC for Expectation Maximization. 2332-2340 - Amirhossein Reisizadeh, Khashayar Gatmiry, Asuman E. Ozdaglar:
EM for Mixture of Linear Regression with Clustered Data. 2341-2349 - Pavel E. Dvurechensky, Jia-Jie Zhu:
Analysis of Kernel Mirror Prox for Measure Optimization. 2350-2358 - Kais Hariz, Hachem Kadri, Stéphane Ayache, Maher Moakher, Thierry Artières:
Implicit Regularization in Deep Tucker Factorization: Low-Rankness via Structured Sparsity. 2359-2367 - Michael Rizvi-Martel, Maude Lizaire, Clara Lacroce, Guillaume Rabusseau:
Simulating weighted automata over sequences and trees with transformers. 2368-2376 - Arnab Auddy, Haolin Zou, Kamiar Rahnama Rad, Arian Maleki:
Approximate Leave-one-out Cross Validation for Regression with ℓ1 Regularizers. 2377-2385 - David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause:
Learning Safety Constraints from Demonstrations with Unknown Rewards. 2386-2394 - Mengxiao Zhang, Haipeng Luo:
Online Learning in Contextual Second-Price Pay-Per-Click Auctions. 2395-2403 - Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon:
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. 2404-2412 - Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. 2413-2421 - T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra:
An Impossibility Theorem for Node Embedding. 2422-2430 - Gian Carlo Diluvi, Benjamin Bloem-Reddy, Trevor Campbell:
Mixed variational flows for discrete variables. 2431-2439 - Shibo Li, Xin Yu, Wei W. Xing, Robert M. Kirby, Akil Narayan, Shandian Zhe:
Multi-Resolution Active Learning of Fourier Neural Operators. 2440-2448 - Kuba Grudzien Kuba, Masatoshi Uehara, Sergey Levine, Pieter Abbeel:
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization. 2449-2457 - Wei-Ning Chen, Graham Cormode, Akash Bharadwaj, Peter Romov, Ayfer Özgür:
Federated Experiment Design under Distributed Differential Privacy. 2458-2466 - Federica Granese, Marco Romanelli, Pablo Piantanida:
Optimal Zero-Shot Detector for Multi-Armed Attacks. 2467-2475 - Sanath Kumar Krishnamurthy, Adrienne M. Propp, Susan Athey:
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective. 2476-2484 - Yash P. Patel, Sahana Rayan, Ambuj Tewari:
Conformal Contextual Robust Optimization. 2485-2493 - Yixin Ren, Yewei Xia, Hao Zhang, Jihong Guan, Shuigeng Zhou:
Learning Adaptive Kernels for Statistical Independence Tests. 2494-2502 - Jacob D. Abernethy, Robert E. Schapire, Umar Syed:
Lexicographic Optimization: Algorithms and Stability. 2503-2511 - Jessica Dai, Bailey Flanigan, Meena Jagadeesan, Nika Haghtalab, Chara Podimata:
Can Probabilistic Feedback Drive User Impacts in Online Platforms? 2512-2520 - Changhao Shi, Gal Mishne:
Learning Cartesian Product Graphs with Laplacian Constraints. 2521-2529 - Rentian Yao, Linjun Huang, Yun Yang:
Minimizing Convex Functionals over Space of Probability Measures via KL Divergence Gradient Flow. 2530-2538 - Samuel Showalter, Alex J. Boyd, Padhraic Smyth, Mark Steyvers:
Bayesian Online Learning for Consensus Prediction. 2539-2547 - Cyrille Kone, Emilie Kaufmann, Laura Richert:
Bandit Pareto Set Identification: the Fixed Budget Setting. 2548-2556 - Jiachen T. Wang, Prateek Mittal, Ruoxi Jia:
Efficient Data Shapley for Weighted Nearest Neighbor Algorithms. 2557-2565 - Vincent Hsiao, Dana S. Nau, Bobak Pezeshki, Rina Dechter:
Surrogate Bayesian Networks for Approximating Evolutionary Games. 2566-2574 - Thiago Ramos, Rodrigo Loro Schuller, Alex Akira Okuno, Lucas Nissenbaum, Roberto I Oliveira, Paulo Orenstein:
BlockBoost: Scalable and Efficient Blocking through Boosting. 2575-2583 - Yan Shen, Zhanghexuan Ji, Chunwei Ma, Mingchen Gao:
Continual Domain Adversarial Adaptation via Double-Head Discriminators. 2584-2592 - Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, Pierre-Luc Bacon:
Maximum entropy GFlowNets with soft Q-learning. 2593-2601 - Arnab Maiti, Ross Boczar, Kevin G. Jamieson, Lillian J. Ratliff:
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits. 2602-2610 - Haoyang Zheng, Wei Deng, Christian Moya, Guang Lin:
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo. 2611-2619 - Kaiwen Wu, Jonathan Wenger, Haydn T. Jones, Geoff Pleiss, Jacob R. Gardner:
Large-Scale Gaussian Processes via Alternating Projection. 2620-2628 - Natalia Martínez, Martín Bertrán, Guillermo Sapiro:
Achieving Group Distributional Robustness and Minimax Group Fairness with Interpolating Classifiers. 2629-2637 - James Leiner, Aaditya Ramdas:
Graph fission and cross-validation. 2638-2646 - Panagiotis Lymperopoulos, Liping Liu:
Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets. 2647-2655 - Yuda Shao, Shan Yu, Tianshu Feng:
Nonparametric Automatic Differentiation Variational Inference with Spline Approximation. 2656-2664 - Eliot Shekhtman, Sarah Dean:
Strategic Usage in a Multi-Learner Setting. 2665-2673 - Huy Nguyen, Khai Nguyen, Nhat Ho:
On Parameter Estimation in Deviated Gaussian Mixture of Experts. 2674-2682 - Huy Nguyen, TrungTin Nguyen, Khai Nguyen, Nhat Ho:
Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts. 2683-2691 - Abhinav Chakraborty, Anirban Chatterjee, Abhinandan Dalal:
PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model. 2692-2700 - Sijin Chen, Zhize Li, Yuejie Chi:
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression. 2701-2709 - Tuan Nguyen, Hirotada Honda, Takashi Sano, Vinh Nguyen, Shugo Nakamura, Tan Minh Nguyen:
From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach. 2710-2718 - Zhishuai Liu, Pan Xu:
Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation. 2719-2727 - Xiaoyang Wang, Dimitrios Dimitriadis, Sanmi Koyejo, Shruti Tople:
Invariant Aggregator for Defending against Federated Backdoor Attacks. 2728-2736 - Zihao Li, Xiang Ji, Minshuo Chen, Mengdi Wang:
Policy Evaluation for Reinforcement Learning from Human Feedback: A Sample Complexity Analysis. 2737-2745 - Arman Adibi, Nicolò Dal Fabbro, Luca Schenato, Sanjeev R. Kulkarni, H. Vincent Poor, George J. Pappas, Hamed Hassani, Aritra Mitra:
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling. 2746-2754 - Maheed H. Ahmed, Mahsa Ghasemi:
Privacy-Preserving Decentralized Actor-Critic for Cooperative Multi-Agent Reinforcement Learning. 2755-2763 - Yunfan Li, Lin Yang:
On the Model-Misspecification in Reinforcement Learning. 2764-2772 - Eitan Levin, Mateo Díaz:
Any-dimensional equivariant neural networks. 2773-2781 - Sara LaPlante, Emilija Perkovic:
Conditional Adjustment in a Markov Equivalence Class. 2782-2790 - Shivvrat Arya, Tahrima Rahman, Vibhav Gogate:
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models. 2791-2799 - Zhaoyang Shi, Krishna Balasubramanian, Wolfgang Polonik:
Adaptive and non-adaptive minimax rates for weighted Laplacian-Eigenmap based nonparametric regression. 2800-2808 - Chris Cundy, Rishi Desai, Stefano Ermon:
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients. 2809-2817 - Shivvrat Arya, Yu Xiang, Vibhav Gogate:
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification. 2818-2826 - Quan M. Nguyen, Nishant Mehta:
Near-optimal Per-Action Regret Bounds for Sleeping Bandits. 2827-2835 - Xinrui Ruan, Jingshen Wang, Yingfei Wang, Waverly Wei:
Electronic Medical Records Assisted Digital Clinical Trial Design. 2836-2844 - Zhi Zhang, Weijian Li, Han Liu:
Multivariate Time Series Forecasting By Graph Attention Networks With Theoretical Guarantees. 2845-2853 - Davoud Ataee Tarzanagh, Parvin Nazari, Bojian Hou, Li Shen, Laura Balzano:
Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods. 2854-2862 - Shibal Ibrahim, Kayhan Behdin, Rahul Mazumder:
End-to-end Feature Selection Approach for Learning Skinny Trees. 2863-2871 - Ryan Thompson, Edwin V. Bonilla, Robert Kohn:
Contextual Directed Acyclic Graphs. 2872-2880 - Yujin Han, Mingwenchan Xu, Leying Guan:
Conformalized Semi-supervised Random Forest for Classification and Abnormality Detection. 2881-2889 - Kei Sen Fong, Mehul Motani:
Multi-Level Symbolic Regression: Function Structure Learning for Multi-Level Data. 2890-2898 - Sijin Chen, Xiwei Cheng, Anthony Man-Cho So:
Non-Convex Joint Community Detection and Group Synchronization via Generalized Power Method. 2899-2907 - Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li:
Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging. 2908-2916 - Jiaxin Zhang, Kamalika Das, Kumar Sricharan:
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods. 2917-2925 - Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho, Samu Kurki, Jussi Leinonen, Miika Koskinen, Samuel Kaski, Harri Lähdesmäki:
Estimating treatment effects from single-arm trials via latent-variable modeling. 2926-2934 - Yiling Kuang, Chao Yang, Yang Yang, Shuang Li:
Unveiling Latent Causal Rules: A Temporal Point Process Approach for Abnormal Event Explanation. 2935-2943 - Ayman Chaouki, Jesse Read, Albert Bifet:
Online Learning of Decision Trees with Thompson Sampling. 2944-2952 - Yu Yang, Eric Gan, Gintare Karolina Dziugaite, Baharan Mirzasoleiman:
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias. 2953-2961 - Subhojyoti Mukherjee, Qiaomin Xie, Josiah P. Hanna, Robert D. Nowak:
SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits. 2962-2970 - Ali Ebrahimpour Boroojeny, Matus Telgarsky, Hari Sundaram:
Spectrum Extraction and Clipping for Implicitly Linear Layers. 2971-2979 - Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu:
Pessimistic Off-Policy Multi-Objective Optimization. 2980-2988 - Søren Wengel Mogensen:
Faithful graphical representations of local independence. 2989-2997 - Ha Manh Bui, Anqi Liu:
Density-Regression: Efficient and Distance-aware Deep Regressor for Uncertainty Estimation under Distribution Shifts. 2998-3006 - Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi:
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures. 3007-3015 - Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten:
On the connection between Noise-Contrastive Estimation and Contrastive Divergence. 3016-3024 - Angela Zhou:
Reward-Relevance-Filtered Linear Offline Reinforcement Learning. 3025-3033 - Ahmad Rashid, Serena Hacker, Guojun Zhang, Agustinus Kristiadi, Pascal Poupart:
Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks. 3034-3042 - Yifu Tang, Yingfei Wang, Zeyu Zheng:
Stochastic Multi-Armed Bandits with Strongly Reward-Dependent Delays. 3043-3051 - Julia Grosse, Rahel Fischer, Roman Garnett, Philipp Hennig:
A Greedy Approximation for k-Determinantal Point Processes. 3052-3060 - Long-Fei Li, Peng Zhao, Zhi-Hua Zhou:
Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition. 3061-3069 - Efe Mert Karagözlü, Yasar Cahit Yildirim, Çagin Ararat, Cem Tekin:
Learning the Pareto Set Under Incomplete Preferences: Pure Exploration in Vector Bandits. 3070-3078 - Hadrien Hendrikx, Paul Mangold, Aurélien Bellet:
The Relative Gaussian Mechanism and its Application to Private Gradient Descent. 3079-3087 - Pim de Haan, Taco Cohen, Johann Brehmer:
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. 3088-3096 - Washim Uddin Mondal, Vaneet Aggarwal:
Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes. 3097-3105 - Hakuei Yamada, Junpei Komiyama, Kenshi Abe, Atsushi Iwasaki:
Learning Fair Division from Bandit Feedback. 3106-3114 - Tam Le, Truyen Nguyen, Kenji Fukumizu:
Optimal Transport for Measures with Noisy Tree Metric. 3115-3123 - Olawale Salaudeen, Sanmi Koyejo:
Causally Inspired Regularization Enables Domain General Representations. 3124-3132 - Victor Dheur, Souhaib Ben Taieb:
Probabilistic Calibration by Design for Neural Network Regression. 3133-3141 - Anna M. Maddux, Maryam Kamgarpour:
Multi-Agent Learning in Contextual Games under Unknown Constraints. 3142-3150 - MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi:
A Scalable Algorithm for Individually Fair k-Means Clustering. 3151-3159 - Yannick Eich, Bastian Alt, Heinz Koeppl:
Approximate Control for Continuous-Time POMDPs. 3160-3168 - Germano Gabbianelli, Gergely Neu, Matteo Papini, Nneka Okolo:
Offline Primal-Dual Reinforcement Learning for Linear MDPs. 3169-3177 - Vincent Souveton, Arnaud Guillin, Jens Jasche, Guilhem Lavaux, Manon Michel:
Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexity. 3178-3186 - Yuqin Yang, Saber Salehkaleybar, Negar Kiyavash:
Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data. 3187-3195 - Jae-Jun Lee, Sung Whan Yoon:
XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage. 3196-3204 - Khaled Eldowa, Andrea Paudice:
General Tail Bounds for Non-Smooth Stochastic Mirror Descent. 3205-3213 - Boris Flach, Dmitrij Schlesinger, Alexander Shekhovtsov:
Symmetric Equilibrium Learning of VAEs. 3214-3222 - Zheng Zhao, Sebastian Mair, Thomas B. Schön, Jens Sjölund:
On Feynman-Kac training of partial Bayesian neural networks. 3223-3231 - Arpan Losalka, Jonathan Scarlett:
No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints. 3232-3240 - Guilherme Augusto Zagatti, See-Kiong Ng, Stéphane Bressan:
Learning multivariate temporal point processes via the time-change theorem. 3241-3249 - Chaoqi Wang, Yuxin Chen, Kevin Murphy:
Model-based Policy Optimization under Approximate Bayesian Inference. 3250-3258 - Xiangyu Zeng, Jie Lin, Piao Hu, Zhihao Li, Tianxi Huang:
SDMTR: A Brain-inspired Transformer for Relation Inference. 3259-3267 - Zitong Ma, Wenbo Zhao, Zhe Yang:
Directed Hypergraph Representation Learning for Link Prediction. 3268-3276 - Zhi Zhang, Chenyu Ma, Saleh Soudijani, Sadegh Soudjani:
Formal Verification of Unknown Stochastic Systems via Non-parametric Estimation. 3277-3285 - Oskar Kviman, Nicola Branchini, Víctor Elvira, Jens Lagergren:
Variational Resampling. 3286-3294 - Tom Sander, Maxime Sylvestre, Alain Durmus:
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training. 3295-3303 - Ivan Peshekhonov, Aleksey Arzhantsev, Maxim V. Rakhuba:
Training a Tucker Model With Shared Factors: a Riemannian Optimization Approach. 3304-3312 - Chaoqi Wang, Ziyu Ye, Kevin Murphy, Yuxin Chen:
Don't Be Pessimistic Too Early: Look K Steps Ahead! 3313-3321 - Jorge García-Carrasco, Alejandro Maté, Juan C. Trujillo:
How does GPT-2 Predict Acronyms? Extracting and Understanding a Circuit via Mechanistic Interpretability. 3322-3330 - Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen:
Identifiable Feature Learning for Spatial Data with Nonlinear ICA. 3331-3339 - Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky:
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data. 3340-3348 - Nisha Chandramoorthy, Florian T. Schaefer, Youssef M. Marzouk:
Score Operator Newton transport. 3349-3357 - Maria C. Novitasari, Johannes Quaas, Miguel Rodrigues:
ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data. 3358-3366 - Alexandre Verine, Muni Sreenivas Pydi, Benjamin Négrevergne, Yann Chevaleyre:
Optimal Budgeted Rejection Sampling for Generative Models. 3367-3375 - Luhuan Wu, Sinead A. Williamson:
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation. 3376-3384 - Wenbo Zhao, Zitong Ma, Zhe Yang:
DHMConv: Directed Hypergraph Momentum Convolution Framework. 3385-3393 - Sebastian Jäger, Felix Biessmann:
From Data Imputation to Data Cleaning - Automated Cleaning of Tabular Data Improves Downstream Predictive Performance. 3394-3402 - Filip Ekström Kelvinius, Fredrik Lindsten:
Discriminator Guidance for Autoregressive Diffusion Models. 3403-3411 - Dongsheng Ding, Zhengyan Huan, Alejandro Ribeiro:
Resilient Constrained Reinforcement Learning. 3412-3420 - Isu Jeong, Seulki Lee:
On-Demand Federated Learning for Arbitrary Target Class Distributions. 3421-3429 - Prarabdh Shukla, Gagan Raj Gupta, Kunal Dutta:
DiffRed: Dimensionality reduction guided by stable rank. 3430-3438 - Ambrus Tamás, Szabolcs Szentpéteri, Balázs Csanád Csáji:
Data-Driven Confidence Intervals with Optimal Rates for the Mean of Heavy-Tailed Distributions. 3439-3447 - Hossein Zakerinia, Shayan Talaei, Giorgi Nadiradze, Dan Alistarh:
Communication-Efficient Federated Learning With Data and Client Heterogeneity. 3448-3456 - Yann Fraboni, Martin Van Waerebeke, Kevin Scaman, Richard Vidal, Laetitia Kameni, Marco Lorenzi:
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization. 3457-3465 - Teodora Popordanoska, Sebastian Gregor Gruber, Aleksei Tiulpin, Florian Büttner, Matthew B. Blaschko:
Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors. 3466-3474 - Dharmesh Tailor, Aditya Patra, Rajeev Verma, Putra Manggala, Eric T. Nalisnick:
Learning to Defer to a Population: A Meta-Learning Approach. 3475-3483 - Kevin Li, Max Balakirsky, Simon Mak:
Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression. 3484-3492 - Ilyas Fatkhullin, Niao He:
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence. 3493-3501 - Bahador Rashidi, Kerrick Johnstonbaugh, Chao Gao:
Cylindrical Thompson Sampling for High-Dimensional Bayesian Optimization. 3502-3510 - Gandharv Patil, Aditya Mahajan, Doina Precup:
On learning history-based policies for controlling Markov decision processes. 3511-3519 - Patrick Kolpaczki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier:
SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. 3520-3528 - Vinod Kumar Chauhan, Jiandong Zhou, Ghadeer O. Ghosheh, Soheila Molaei, David A. Clifton:
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation. 3529-3537 - Alexandra Maria Hotti, Lennart Alexander Van der Goten, Jens Lagergren:
Benefits of Non-Linear Scale Parameterizations in Black Box Variational Inference through Smoothness Results and Gradient Variance Bounds. 3538-3546 - Volodimir Mitarchuk, Clara Lacroce, Rémi Eyraud, Rémi Emonet, Amaury Habrard, Guillaume Rabusseau:
Length independent PAC-Bayes bounds for Simple RNNs. 3547-3555 - Hristo Papazov, Scott Pesme, Nicolas Flammarion:
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks. 3556-3564 - Atsushi Nitanda, Ryuhei Kikuchi, Shugo Maeda, Denny Wu:
Why is parameter averaging beneficial in SGD? An objective smoothing perspective. 3565-3573 - Ryusei Shingaki, Manabu Kuroki:
Identification and Estimation of "Causes of Effects" using Covariate-Mediator Information. 3574-3582 - Élise Crepon, Aurélien Garivier, Wouter M. Koolen:
Sequential learning of the Pareto front for multi-objective bandits. 3583-3591 - Diptarka Chakraborty, Sourav Chakraborty, Gunjan Kumar, Kuldeep S. Meel:
Equivalence Testing: The Power of Bounded Adaptivity. 3592-3600 - Krzysztof Kacprzyk, Mihaela van der Schaar:
Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations. 3601-3609 - Weichen Wu, Jisu Kim, Alessandro Rinaldo:
On the estimation of persistence intensity functions and linear representations of persistence diagrams. 3610-3618 - Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya:
Optimal estimation of Gaussian (poly)trees. 3619-3627 - Thomas L. Lee, Amos J. Storkey:
Approximate Bayesian Class-Conditional Models under Continuous Representation Shift. 3628-3636 - Paolo Battellani, Alberto Maria Metelli, Francesco Trovò:
Dissimilarity Bandits. 3637-3645 - Piyushi Manupriya, Rachit Keerti Das, Sayantan Biswas, Saketha Nath Jagarlapudi:
Consistent Optimal Transport with Empirical Conditional Measures. 3646-3654 - Simon Martin, Francis Bach, Giulio Biroli:
On the Impact of Overparameterization on the Training of a Shallow Neural Network in High Dimensions. 3655-3663 - Jan P. Engelmann, Alessandro Palma, Jakub M. Tomczak, Fabian J. Theis, Francesco Paolo Casale:
Mixed Models with Multiple Instance Learning. 3664-3672 - Jiayi Huang, Han Zhong, Liwei Wang, Lin Yang:
Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation. 3673-3681 - Konstantinos Emmanouilidis, René Vidal, Nicolas Loizou:
Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities. 3682-3690 - Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam:
Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models. 3691-3699 - Iden Kalemaj, Shiva Prasad Kasiviswanathan, Aaditya Ramdas:
Differentially Private Conditional Independence Testing. 3700-3708 - Kevin Scaman, Mathieu Even, Batiste Le Bars, Laurent Massoulié:
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles. 3709-3717 - Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher, Mikhail Belkin:
On the Nyström Approximation for Preconditioning in Kernel Machines. 3718-3726 - Fahad Kamran, Maggie Makar, Jenna Wiens:
Learning to Rank for Optimal Treatment Allocation Under Resource Constraints. 3727-3735 - Alex Oesterling, Jiaqi Ma, Flávio P. Calmon, Himabindu Lakkaraju:
Fair Machine Unlearning: Data Removal while Mitigating Disparities. 3736-3744 - Yipei Wang, Xiaoqian Wang:
On the Effect of Key Factors in Spurious Correlation: A theoretical Perspective. 3745-3753 - Maosheng Yang, Viacheslav Borovitskiy, Elvin Isufi:
Hodge-Compositional Edge Gaussian Processes. 3754-3762 - Asterios Tsiourvas, Wei Sun, Georgia Perakis:
Manifold-Aligned Counterfactual Explanations for Neural Networks. 3763-3771 - Jialun Zhang, Richard Y. Zhang, Hong-Ming Chiu:
Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent. 3772-3780 - Chaitanya Murti, Dhruva Kashyap, Chiranjib Bhattacharyya:
LP-based Construction of DC Decompositions for Efficient Inference of Markov Random Fields. 3781-3789 - Achille O. R. Nazaret, Claudia Shi, David M. Blei:
On the Misspecification of Linear Assumptions in Synthetic Controls. 3790-3798 - Daniel Carmon, Amir Yehudayoff, Roi Livni:
The sample complexity of ERMs in stochastic convex optimization. 3799-3807 - Amey P. Pasarkar, Adji Bousso Dieng:
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning. 3808-3816 - Liwei Wang, Xinru Liu, Aaron Smith, Aguemon Y. Atchadé:
On cyclical MCMC sampling. 3817-3825 - Joshua John Ward, Xianli Zeng, Guang Cheng:
FairRR: Pre-Processing for Group Fairness through Randomized Response. 3826-3834 - Yin Liu, Sam Davanloo Tajbakhsh:
Fitting ARMA Time Series Models without Identification: A Proximal Approach. 3835-3843 - Daoping Wu, Suhas Gundimeda, Shaoshuai Mou, Christopher J. Quinn:
Unsupervised Change Point Detection in Multivariate Time Series. 3844-3852 - Damien Ferbach, Baptiste Goujaud, Gauthier Gidel, Aymeric Dieuleveut:
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport. 3853-3861 - Yinuo Ren, Tesi Xiao, Tanmay Gangwani, Anshuka Rangi, Holakou Rahmanian, Lexing Ying, Subhajit Sanyal:
Multi-objective Optimization via Wasserstein-Fisher-Rao Gradient Flow. 3862-3870 - Thomas Guilmeau, Nicola Branchini, Emilie Chouzenoux, Victor Elvira:
Adaptive importance sampling for heavy-tailed distributions via α-divergence minimization. 3871-3879 - Mehdi Jafarnia-Jahromi, Rahul A. Jain, Ashutosh Nayyar:
A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent. 3880-3888 - Yang Cai, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng:
Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games. 3889-3897 - Osama A. Hanna, Merve Karakas, Lin Yang, Christina Fragouli:
Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels. 3898-3906 - Jianwei Shen, Jason Pacheco:
Efficient Variational Sequential Information Control. 3907-3915 - Jonathan Colaço Carr, Prakash Panangaden, Doina Precup:
Conditions on Preference Relations that Guarantee the Existence of Optimal Policies. 3916-3924 - Jiaqi Zhang, Kirankumar Shiragur, Caroline Uhler:
Membership Testing in Markov Equivalence Classes via Independence Queries. 3925-3933 - Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth:
Functional Flow Matching. 3934-3942 - Kirk C. Bansak, Elisabeth Paulson, Dominik Rothenhäusler:
Learning Under Random Distributional Shifts. 3943-3951 - Kaiting Liu, Zahra Atashgahi, Ghada Sokar, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Supervised Feature Selection via Ensemble Gradient Information from Sparse Neural Networks. 3952-3960 - Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton:
Proxy Methods for Domain Adaptation. 3961-3969 - Kyra Gan, Esmaeil Keyvanshokooh, Xueqing Liu, Susan A. Murphy:
Contextual Bandits with Budgeted Information Reveal. 3970-3978 - Helen Zhou, Audrey Huang, Kamyar Azizzadenesheli, David Childers, Zachary C. Lipton:
Timing as an Action: Learning When to Observe and Act. 3979-3987 - Wei Shen, Minhui Huang, Jiawei Zhang, Cong Shen:
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization. 3988-3996 - Ziyu Xu, Aaditya Ramdas:
Online multiple testing with e-values. 3997-4005 - Rayen Tan, Rohan Ghuge, Viswanath Nagarajan:
Informative Path Planning with Limited Adaptivity. 4006-4014 - Kai Lion, Lorenzo Noci, Thomas Hofmann, Gregor Bachmann:
How Good is a Single Basin? 4015-4023 - Philip Jordan, Anas Barakat, Niao He:
Independent Learning in Constrained Markov Potential Games. 4024-4032 - N. Benjamin Erichson, Soon Hoe Lim, Winnie Xu, Francisco Utrera, Ziang Cao, Michael W. Mahoney:
NoisyMix: Boosting Model Robustness to Common Corruptions. 4033-4041 - Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt:
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection. 4042-4050 - Georg Pichler, Marco Romanelli, Divya Prakash Manivannan, Prashanth Krishnamurthy, Farshad Khorrami, Siddharth Garg:
On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem. 4051-4059 - Tyler Maunu, Martin Molina-Fructuoso:
Acceleration and Implicit Regularization in Gaussian Phase Retrieval. 4060-4068 - Miruna Oprescu, Andrew Bennett, Nathan Kallus:
Low-rank MDPs with Continuous Action Spaces. 4069-4077 - Alex Zhai, Dee Guo, Sreenivas Gollapudi, Kostas Kollias, Daniel Delling:
Deep Learning-Based Alternative Route Computation. 4078-4086 - Oren Wright, Yorie Nakahira, José M. F. Moura:
An Analytic Solution to Covariance Propagation in Neural Networks. 4087-4095 - Avrim Blum, Princewill Okoroafor, Aadirupa Saha, Kevin M. Stangl:
On the Vulnerability of Fairness Constrained Learning to Malicious Noise. 4096-4104 - Siyuan Xu, Yucheng Wang, Mingzhou Fan, Byung-Jun Yoon, Xiaoning Qian:
Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting. 4105-4113 - Kenny Olsen, Rasmus M. Hoeegh Lindrup, Morten Mørup:
Think Global, Adapt Local: Learning Locally Adaptive K-Nearest Neighbor Kernel Density Estimators. 4114-4122 - Emmanouil-Vasileios Vlatakis-Gkaragkounis, Angeliki Giannou, Yudong Chen, Qiaomin Xie:
Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements. 4123-4131 - Philipp Michael Faller, Leena C. Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing:
Self-Compatibility: Evaluating Causal Discovery without Ground Truth. 4132-4140 - Marcelo Pereyra, Julián Tachella:
Equivariant bootstrapping for uncertainty quantification in imaging inverse problems. 4141-4149 - Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang:
Private Learning with Public Features. 4150-4158 - Alessio Mazzetto:
An Improved Algorithm for Learning Drifting Discrete Distributions. 4159-4167 - Jinyeong Chae, Donghwa Kim, Kwanseok Kim, Doyeon Lee, Sangho Lee, Seongsu Ha, Jonghwan Mun, Wooyoung Kang, Byungseok Roh, Joonseok Lee:
Towards a Complete Benchmark on Video Moment Localization. 4168-4176 - Neharika Jali, Guannan Qu, Weina Wang, Gauri Joshi:
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems. 4177-4185 - Mohammad Reza Karimi, Ya-Ping Hsieh, Andreas Krause:
Sinkhorn Flow as Mirror Flow: A Continuous-Time Framework for Generalizing the Sinkhorn Algorithm. 4186-4194 - Zongyu Dai, Emily J. Getzen, Qi Long:
SADI: Similarity-Aware Diffusion Model-Based Imputation for Incomplete Temporal EHR Data. 4195-4203 - Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien:
Weight-Sharing Regularization. 4204-4212 - Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov:
Generative Flow Networks as Entropy-Regularized RL. 4213-4221 - Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Mark Coates:
Multi-resolution Time-Series Transformer for Long-term Forecasting. 4222-4230 - Kritkorn Karntikoon, Yiheng Shen, Sreenivas Gollapudi, Kostas Kollias, Aaron Schild, Ali Kemal Sinop:
First Passage Percolation with Queried Hints. 4231-4239 - Daogao Liu, Hilal Asi:
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates. 4240-4248 - Hamza Giaffar, Camille E. Rullán Buxó, Mikio Aoi:
The Effective Number of Shared Dimensions Between Paired Datasets. 4249-4257 - Zhishang Luo, Truong Son Hy, Puoya Tabaghi, Michaël Defferrard, Elahe Rezaei, Ryan Carey, W. Rhett Davis, Rajeev Jain, Yusu Wang:
DE-HNN: An effective neural model for Circuit Netlist representation. 4258-4266 - Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke:
Simulation-Based Stacking. 4267-4275 - Ziyu Gong, Ben Usman, Han Zhao, David I. Inouye:
Towards Practical Non-Adversarial Distribution Matching. 4276-4284 - Ilker Demirel, Edward De Brouwer, Zeshan M. Hussain, Michael Oberst, Anthony Philippakis, David A. Sontag:
Benchmarking Observational Studies with Experimental Data under Right-Censoring. 4285-4293 - Vasileios Kalantzis, Shashanka Ubaru, Chai Wah Wu, Georgios Kollias, Lior Horesh:
Asynchronous Randomized Trace Estimation. 4294-4302 - George Z. Li, Dung Nguyen, Anil Vullikanti:
Computing epidemic metrics with edge differential privacy. 4303-4311 - Declan McNamara, Jackson Loper, Jeffrey Regier:
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference. 4312-4320 - Jeremy McMahan, Xiaojin Zhu:
Anytime-Constrained Reinforcement Learning. 4321-4329 - Tao Wen, Elynn Chen, Yuzhou Chen:
Tensor-view Topological Graph Neural Network. 4330-4338 - Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton, Bryan Wilder:
Auditing Fairness under Unobserved Confounding. 4339-4347 - Samson J. Koelle, Hanyu Zhang, Octavian-Vlad Murad, Marina Meila:
Consistency of Dictionary-Based Manifold Learning. 4348-4356 - Yuxin Chang, Alex J. Boyd, Padhraic Smyth:
Probabilistic Modeling for Sequences of Sets in Continuous-Time. 4357-4365 - Hui Lan, Vasilis Syrgkanis:
Causal Q-Aggregation for CATE Model Selection. 4366-4374 - Kaiqi Zhao, Ming Zhao:
Self-Supervised Quantization-Aware Knowledge Distillation. 4375-4383 - Xiang Meng, Wenyu Chen, Riade Benbaki, Rahul Mazumder:
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning. 4384-4392 - Yinglong Guo, Shaohan Li, Gilad Lerman:
The effect of Leaky ReLUs on the training and generalization of overparameterized networks. 4393-4401 - Hongchang Gao:
Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate. 4402-4410 - Ruichen Jiang, Parameswaran Raman, Shoham Sabach, Aryan Mokhtari, Mingyi Hong, Volkan Cevher:
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate. 4411-4419 - Wesley Suttle, Vipul Kumar Sharma, Krishna Chaitanya Kosaraju, Seetharaman Sivaranjani, Ji Liu, Vijay Gupta, Brian M. Sadler:
Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems. 4420-4428 - Jhanvi Garg, Xianyang Zhang, Quan Zhou:
Soft-constrained Schrödinger Bridge: a Stochastic Control Approach. 4429-4437 - Naitong Chen, Trevor Campbell:
Coreset Markov chain Monte Carlo. 4438-4446 - Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rémi Munos, Mark Rowland, Michal Valko, Daniele Calandriello:
A General Theoretical Paradigm to Understand Learning from Human Preferences. 4447-4455 - Myrl G. Marmarelis, Fred Morstatter, Aram Galstyan, Greg Ver Steeg:
Policy Learning for Localized Interventions from Observational Data. 4456-4464 - Yinuo Ren, Chao Ma, Lexing Ying:
Understanding the Generalization Benefits of Late Learning Rate Decay. 4465-4473 - Junghyun Lee, Se-Young Yun, Kwang-Sung Jun:
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion. 4474-4482 - Yutong Wang, Rishi Sonthalia, Wei Hu:
Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and Generalization. 4483-4491 - Manav Kant, Eric Y. Ma, Andrei Staicu, Leonard J. Schulman, Spencer Gordon:
Identifiability of Product of Experts Models. 4492-4500 - Haobo Chen, Gregory W. Wornell, Yuheng Bu:
Gibbs-Based Information Criteria and the Over-Parameterized Regime. 4501-4509 - Bhagyashree Puranik, Ahmad Beirami, Yao Qin, Upamanyu Madhow:
Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective. 4510-4518 - Yuyang Deng, Junyuan Hong, Jiayu Zhou, Mehrdad Mahdavi:
On the Generalization Ability of Unsupervised Pretraining. 4519-4527 - Waleed Mustafa, Philipp Liznerski, Antoine Ledent, Dennis Wagner, Puyu Wang, Marius Kloft:
Non-vacuous Generalization Bounds for Adversarial Risk in Stochastic Neural Networks. 4528-4536 - Charles Xu, Laney Goldman, Valentina Guo, Benjamin Hollander-Bodie, Maedee Trank-Greene, Ian Adelstein, Edward De Brouwer, Rex Ying, Smita Krishnaswamy, Michael Perlmutter:
BLIS-Net: Classifying and Analyzing Signals on Graphs. 4537-4545 - Rohan Deb, Aadirupa Saha, Arindam Banerjee:
Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources. 4546-4554 - Houston Warren, Fabio Ramos:
Fast Fourier Bayesian Quadrature. 4555-4563 - Yu Inatsu, Shion Takeno, Hiroyuki Hanada, Kazuki Iwata, Ichiro Takeuchi:
Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty. 4564-4572 - Cyrus Cousins, I. Elizabeth Kumar, Suresh Venkatasubramanian:
To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models. 4573-4581 - Lingkai Kong, Haotian Sun, Yuchen Zhuang, Haorui Wang, Wenhao Mu, Chao Zhang:
Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process. 4582-4590 - Taemin Heo, Chandrajit Bajaj:
Sample Efficient Learning of Factored Embeddings of Tensor Fields. 4591-4599 - Miguel Biron-Lattes, Nikola Surjanovic, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté:
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm. 4600-4608 - Zirui Yan, Dennis Wei, Dmitriy A. Katz, Prasanna Sattigeri, Ali Tajer:
Causal Bandits with General Causal Models and Interventions. 4609-4617 - Nathan Wycoff:
Surrogate Active Subspaces for Jump-Discontinuous Functions. 4618-4626 - Ahmet Alacaoglu, Stephen J. Wright:
Complexity of Single Loop Algorithms for Nonlinear Programming with Stochastic Objective and Constraints. 4627-4635 - Mishfad Shaikh Veedu, Deepjyoti Deka, Murti V. Salapaka:
Information Theoretically Optimal Sample Complexity of Learning Dynamical Directed Acyclic Graphs. 4636-4644 - Seongwoo Lim, Won Jo, Joohyung Lee, Jaesik Choi:
Pathwise Explanation of ReLU Neural Networks. 4645-4653 - John Hood, Aaron J. Schein:
The ALℓ0CORE Tensor Decomposition for Sparse Count Data. 4654-4662 - Feihu Huang, Xinrui Wang, Junyi Li, Songcan Chen:
Adaptive Federated Minimax Optimization with Lower Complexities. 4663-4671 - Ronghao Ni, Zinan Lin, Shuaiqi Wang, Giulia Fanti:
Mixture-of-Linear-Experts for Long-term Time Series Forecasting. 4672-4680 - Ali Mortazavi, Junhao Lin, Nishant A. Mehta:
On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: a regret lower bound for WSU-UX. 4681-4689 - Princewill Okoroafor, Robert D. Kleinberg, Wen Sun:
Faster Recalibration of an Online Predictor via Approachability. 4690-4698 - Min Cheng, Ruida Zhou, P. R. Kumar, Chao Tian:
Provable Policy Gradient Methods for Average-Reward Markov Potential Games. 4699-4707 - Mizhaan Prajit Maniyar, Prashanth L. A., Akash Mondal, Shalabh Bhatnagar:
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning. 4708-4716 - Juanwu Lu, Wei Zhan, Masayoshi Tomizuka, Yeping Hu:
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach. 4717-4725 - Muhammed Emrullah Ildiz, Zhe Zhao, Samet Oymak:
Understanding Inverse Scaling and Emergence in Multitask Representation Learning. 4726-4734 - Aakash Sunil Lahoti, Spandan Senapati, Ketan Rajawat, Alec Koppel:
Sharpened Lazy Incremental Quasi-Newton Method. 4735-4743 - Yinan Li, Chicheng Zhang:
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach. 4744-4752 - Anqi Mao, Mehryar Mohri, Yutao Zhong:
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention. 4753-4761 - Steven Braun, Martin Mundt, Kristian Kersting:
Deep Classifier Mimicry without Data Access. 4762-4770 - Velibor Bojkovic, Srinivas Anumasa, Giulia De Masi, Bin Gu, Huan Xiong:
Data Driven Threshold and Potential Initialization for Spiking Neural Networks. 4771-4779 - Barak Battash, Lior Wolf, Ofir Lindenbaum:
Revisiting the Noise Model of Stochastic Gradient Descent. 4780-4788 - Masahiro Nakano, Hiroki Sakuma, Ryo Nishikimi, Ryohei Shibue, Takashi Sato, Tomoharu Iwata, Kunio Kashino:
Warped Diffusion for Latent Differentiation Inference. 4789-4797 - Nikita Tsoy, Anna Mihalkova, Teodora N. Todorova, Nikola Konstantinov:
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains. 4798-4806 - Andrea Ceni, Andrea Cossu, Maximilian W. Stölzle, Jingyue Liu, Cosimo Della Santina, Davide Bacciu, Claudio Gallicchio:
Random Oscillators Network for Time Series Processing. 4807-4815 - Shuqi Liu, Yuzhou Cao, Qiaozhen Zhang, Lei Feng, Bo An:
Mitigating Underfitting in Learning to Defer with Consistent Losses. 4816-4824 - Yuzhou Cao, Lei Feng, Bo An:
Consistent Hierarchical Classification with A Generalized Metric. 4825-4833 - Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting, Frank Proske, Aurélien Lucchi:
SDEs for Minimax Optimization. 4834-4842 - Sayak Ray Chowdhury, Xingyu Zhou, Nagarajan Natarajan:
Differentially Private Reward Estimation with Preference Feedback. 4843-4851 - Nikita Morozov, Denis Rakitin, Oleg Desheulin, Dmitry P. Vetrov, Kirill Struminsky:
Differentiable Rendering with Reparameterized Volume Sampling. 4852-4860 - Florian Hübler, Junchi Yang, Xiang Li, Niao He:
Parameter-Agnostic Optimization under Relaxed Smoothness. 4861-4869 - Ruslan Nazykov, Aleksandr Shestakov, Vladimir Solodkin, Aleksandr Beznosikov, Gauthier Gidel, Alexander V. Gasnikov:
Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases. 4870-4878 - Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov:
Efficient Conformal Prediction under Data Heterogeneity. 4879-4887 - Sanmitra Ghosh, Paul Birrell, Daniela De Angelis:
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs. 4888-4896 - Sarah Mameche, Jilles Vreeken, David Kaltenpoth:
Identifying Confounding from Causal Mechanism Shifts. 4897-4905 - Ben Batten, Mehran Hosseini, Alessio Lomuscio:
Tight Verification of Probabilistic Robustness in Bayesian Neural Networks. 4906-4914 - Aytijhya Saha, Aaditya Ramdas:
Testing exchangeability by pairwise betting. 4915-4923
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