7 citations · 27 across the 14 of their papers we have counts for
15 papers
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…
Koopman Methods for Estimation of Animal Motions over Unknown Submanifolds
Nathan Powell, Bowei Liu, Jia Guo +2
This paper introduces a data-dependent approximation of the forward kinematics map for certain types of animal motion models. It is assumed that motions are supported on a low-dime…
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…
Learning Theory for Estimation of Animal Motion Submanifolds
Nathan Powell, Andrew Kurdila
This paper describes the formulation and experimental testing of a novel method for the estimation and approximation of submanifold models of animal motion. It is assumed that the…
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…
Koopman Theory and Linear Approximation Spaces
Andrew J. Kurdila, Parag Bobade
Koopman theory studies dynamical systems in terms of operator theoretic properties of the Perron-Frobenius operator and Koopman operator respectively. I…
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…
Online Estimation and Adaptive Control for a Class of History Dependent Functional Differential Equations
Shirin Dadashi, Parag Bobade, Andrew Kurdila
This paper presents sufficient conditions for the convergence of online estimation methods and the stability of adaptive control strategies for a class of history dependent, functi…