7 citations · 20 across the 12 of their papers we have counts for
12 papers
Tailoring Reproducing Kernels for Optimal Control via Policy Iteration
Shengyuan Niu, Ali Bouland, Haoran Wang +5
This paper presents a novel approach to formulating the actor-critic method for optimal control by casting policy iteration in reproducing kernel Hilbert spaces (RKHSs -- also know…
Convergence Rates of Online Critic Value Function Approximation in Native Spaces
Shengyuan Niu, Ali Bouland, Haoran Wang +5
In this paper, the evolution equation that defines the online critic for the approximation of the optimal value function is cast in a general class of reproducing kernel Hilbert sp…
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…
Kernel Methods for Regression in Continuous Time over Subsets and Manifolds
Nathan Powell, Jia Guo, Sai Tej Parachuri +3
This paper derives error bounds for regression in continuous time over subsets of certain types of Riemannian manifolds.The regression problem is typically driven by a nonlinear ev…
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…
Approximations of the Reproducing Kernel Hilbert Space (RKHS) Embedding Method over Manifolds
Jia Guo, Sai Tej Paruchuri, Andrew J. Kurdila
The reproducing kernel Hilbert space (RKHS) embedding method is a recently introduced estimation approach that seeks to identify the unknown or uncertain function in the governing…
Intrinsic and Extrinsic Approximation of Koopman Operators over Manifolds
Sai Tej Paruchuri, Jia Guo, Michael Kepler +4
This paper derives rates of convergence of certain approximations of the Koopman operators that are associated with discrete, deterministic, continuous semiflows on a complete metr…
RKHS Embedding for Estimating Nonlinear Piezoelectric Systems
Sai Tej Paruchuri, Jia Guo, Andrew J. Kurdila
Nonlinearities in piezoelectric systems can arise from internal factors such as nonlinear constitutive laws or external factors like realizations of boundary conditions. It can be…
Partial Persistence of Excitation in RKHS Embedded Adaptive Estimation
Jia Guo, Sai Tej Paruchuri, Andrew J. Kurdila
In this paper, an adaptive non-parametric method is proposed to estimate the scalar-valued nonlinear function that appears in uncertain systems governed by ordinary differential eq…
Persistence of Excitation in Reproducing Kernel Hilbert Spaces, Positive Limit Sets, and Smooth Manifolds
Andrew J. Kurdila, Jia Guo, Sai Tej Paruchuri +1
This paper studies the relationship between the positive limit sets of continuous semiflows and the newly introduced definition of persistently excited (PE) sets and associated sub…