Papers
Concentration of Cumulative Reward in Markov Decision Processes
Borna Sayedana, Peter E. Caines, Aditya Mahajan
Combining Climate Models using Bayesian Regression Trees and Random Paths
John C. Yannotty, Thomas J. Santner, Bo Li et al.
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs
Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov et al.
Physics-Informed Deep Learning and Compressive Collocation for High-Dimensional Diffusion-Reaction Equations: Practical Existence Theory and Numerics
Simone Brugiapaglia, Nick Dexter, Samir Karam et al.
Substitute Adjustment via Recovery of Latent Variables
Jeffrey Adams, Niels Richard Hansen
Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult
Yuqing Wang, Zhenghao Xu, Tuo Zhao et al.
FedHB: Hierarchical Bayesian Federated Learning
Minyoung Kim, Timothy Hospedales
Sketching in High-Dimensional Regression With Big Data Using Gaussian Scale Mixture Priors
Rajarshi Guhaniyogi, Aaron Wolfe Scheffler
Optimal Functional Bilinear Regression with Two-dimensional Functional Covariates via Reproducing Kernel Hilbert Space
Dan Yang, Jianlong Shao, Haipeng Shen et al.
Towards Understanding Gradient Flow Dynamics of Homogeneous Neural Networks Beyond the Origin
Akshay Kumar, Jarvis Haupt
Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity
Qiankun Shi, Jie Peng, Kun Yuan et al.
Towards Unified Native Spaces in Kernel Methods
Xavier Emery, Emilio Porcu, Moreno Bevilacqua
TorchCP: A Python Library for Conformal Prediction
Jianguo Huang, Jianqing Song, Xuanning Zhou et al.
Hopfield-Fenchel-Young Networks: A Unified Framework for Associative Memory Retrieval
Saul Santos, Vlad Niculae, Daniel McNamee et al.
Identifiability of Causal Graphs under Non-Additive Conditionally Parametric Causal Models
Juraj Bodik, Valérie Chavez-Demoulin
Fundamental Limits of Membership Inference Attacks on Machine Learning Models
Eric Aubinais, Elisabeth Gassiat, Pablo Piantanida
On the Robustness of Kernel Goodness-of-Fit Tests
Xing Liu, François-Xavier Briol
Efficient Online Prediction for High-Dimensional Time Series via Joint Tensor Tucker Decomposition
Zhenting Luan, Defeng Sun, Haoning Wang et al.
Collaborative likelihood-ratio estimation over graphs
Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent
Rahul Singh, Abhinek Shukla, Dootika Vats
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies
Zhanyu Wang, Guang Cheng, Jordan Awan
Convergence and Sample Complexity of Natural Policy Gradient Primal-Dual Methods for Constrained MDPs
Dongsheng Ding, Kaiqing Zhang, Jiali Duan et al.
Differentially Private Multivariate Medians
Kelly Ramsay, Aukosh Jagannath, Shoja'eddin Chenouri