7 citations · 27 across the 14 of their papers we have counts for
5 papers · 1 filter
Rates of Convergence in Certain Native Spaces of Approximations used in Reinforcement Learning
Ali Bouland, Shengyuan Niu, Sai Tej Paruchuri +3
This paper studies convergence rates for some value function approximations that arise in a collection of reproducing kernel Hilbert spaces (RKHS) . By casting an optimal con…
Strictly Decentralized Adaptive Estimation of External Fields using Reproducing Kernels
Jia Guo, Michael E. Kepler, Sai Tej Paruchuri +3
This paper describes an adaptive method in continuous time for the estimation of external fields by a team of agents. The agents each explore subdomains of a bounded…
Kernel Center Adaptation in the Reproducing Kernel Hilbert Space Embedding Method
Sai Tej Paruchuri, Jia Guo, Andrew Kurdila
The performance of adaptive estimators that employ embedding in reproducing kernel Hilbert spaces (RKHS) depends on the choice of the location of basis kernel centers. Parameter co…
Sufficient Conditions for Parameter Convergence over Embedded Manifolds using Kernel Techniques
Sai Tej Paruchuri, Jia Guo, Andrew Kurdila
The persistence of excitation (PE) condition is sufficient to ensure parameter convergence in adaptive estimation problems. Recent results on adaptive estimation in reproducing ker…
Adaptive Estimation for Nonlinear Systems using Reproducing Kernel Hilbert Spaces
Parag Bobade, Suprotim Majumdar, Savio Pereira +2
This paper extends a conventional, general framework for online adaptive estimation problems for systems governed by unknown nonlinear ordinary differential equations. The central…