602 citations · 2k across the 59 of their papers we have counts for
60 papers
Nyström Kernel Stein Discrepancy Tests
Florian Kalinke, Zoltán Szabó, Bharath K. Sriperumbudur
Kernel Stein discrepancy (KSD) is among the most popular goodness-of-fit (GoF) measures on general domains with a large number of successful deployments. One of the main applicatio…
Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer
Aratrika Mustafi, Soumya Mukherjee, Bharath K. Sriperumbudur
We develop a gradient flow on the space of probability measures defined on matrix-valued parameters induced by regularized Muon, an analytically smoothed version of the idealized M…
Sobolev Regularized MMD Gradient Flow
Chenyang Tian, Bharath K. Sriperumbudur, Arthur Gretton +1
We propose Sobolev-regularized Maximum Mean Discrepancy (SrMMD) gradient flow, a regularized variant of maximum mean discrepancy (MMD) gradient flow based on a gradient penalty on…
Kernel Single-Index Bandits: Estimation, Inference, and Learning
Sakshi Arya, Satarupa Bhattacharjee, Bharath K. Sriperumbudur
We study contextual bandits with finitely many actions in which the reward of each arm follows a single-index model with an arm-specific index parameter and an unknown nonparametri…
Minimax Optimal Estimation of Mean and Covariance Functions with Spectral Regularization
Naveen Gupta, Bharath K Sriperumbudur
Estimation of the mean and covariance functions is a fundamental problem in functional data analysis, particularly for discretely observed functional data. In this work, we study a…
Gaussian Processes and Reproducing Kernel Hilbert Spaces: Connections and Equivalences
Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic +1
This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, and non-probabilistic methods using rep…