Papers
4,122 papers found
Combining Climate Models using Bayesian Regression Trees and Random Paths
John C. Yannotty, Thomas J. Santner, Bo Li et al.
Composite Goodness-of-fit Tests with Kernels
Oscar Key, Arthur Gretton, François-Xavier Briol et al.
Concentration of Cumulative Reward in Markov Decision Processes
Borna Sayedana, Peter E. Caines, Aditya Mahajan
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
Jannis Chemseddine, Paul Hagemann, Gabriele Steidl et al.
Contextual Bandits with Stage-wise Constraints
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett
Continuously evolving rewards in an open-ended environment
Richard M. Bailey
Continuum Attention for Neural Operators
Edoardo Calvello, Nikola B. Kovachki, Matthew E. Levine et al.
Convergence and Sample Complexity of Natural Policy Gradient Primal-Dual Methods for Constrained MDPs
Dongsheng Ding, Kaiqing Zhang, Jiali Duan et al.
Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation
David Holzmüller, Francis Bach
Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding
Jiajing Zheng, Alexander D'Amour, Alexander Franks
Curvature-based Clustering on Graphs
Yu Tian, Zachary Lubberts, Melanie Weber
DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning
Xiangdong Xie, Jiahua Guo, Yi Sun
Data-Driven Performance Guarantees for Classical and Learned Optimizers
Rajiv Sambharya, Bartolomeo Stellato
Decentralized Asynchronous Optimization with DADAO allows Decoupling and Acceleration
Adel Nabli, Edouard Oyallon
Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity
Boao Kong, Shuchen Zhu, Songtao Lu et al.
Decentralized Sparse Linear Regression via Gradient-Tracking
Marie Maros, Gesualdo Scutari, Ying Sun et al.
DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning
Sarah Segel, Helena Graf, Edward Bergman et al.
Deep Generative Models: Complexity, Dimensionality, and Approximation
Kevin Wang, Hongqian Niu, Yixin Wang et al.
Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation
Hao Liu, Jiahui Cheng, Wenjing Liao
Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization
Antoine de Mathelin, François Deheeger, Mathilde Mougeot et al.
Deep Variational Multivariate Information Bottleneck - A Framework for Variational Losses
Eslam Abdelaleem, Ilya Nemenman, K. Michael Martini
Degree of Interference: A General Framework For Causal Inference Under Interference
Yuki Ohnishi, Bikram Karmakar, Arman Sabbaghi
Deletion Robust Non-Monotone Submodular Maximization over Matroids
Paul Dütting, Federico Fusco, Silvio Lattanzi et al.
Density Estimation Using the Perceptron
Patrik Róbert Gerber, Tianze Jiang, Yury Polyanskiy et al.
depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers
Kaichao You, Runsheng Bai, Meng Cao et al.