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27th 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