1 citations · 2 across the 5 of their papers we have counts for
5 papers
Approximation with Random Shallow ReLU Networks with Applications to Model Reference Adaptive Control
Andrew Lamperski, Tyler Lekang
Neural networks are regularly employed in adaptive control of nonlinear systems and related methods of reinforcement learning. A common architecture uses a neural network with a si…
Function Approximation with Randomly Initialized Neural Networks for Approximate Model Reference Adaptive Control
Tyler Lekang, Andrew Lamperski
Classical results in neural network approximation theory show how arbitrary continuous functions can be approximated by networks with a single hidden layer, under mild assumptions…
Sufficient Conditions for Persistency of Excitation with Step and ReLU Activation Functions
Tyler Lekang, Andrew Lamperski
This paper defines geometric criteria which are then used to establish sufficient conditions for persistency of excitation with vector functions constructed from single hidden-laye…
Wasserstein Contraction Bounds on Closed Convex Domains with Applications to Stochastic Adaptive Control
Tyler Lekang, Andrew Lamperski
This paper is motivated by the problem of quantitatively bounding the convergence of adaptive control methods for stochastic systems to a stationary distribution. Such bounds are u…
Simple Algorithms for Dueling Bandits
Tyler Lekang, Andrew Lamperski
In this paper, we present simple algorithms for Dueling Bandits. We prove that the algorithms have regret bounds for time horizon T of order O(T^rho ) with 1/2 <= rho <= 3/4, which…