Approximation with Random Shallow ReLU Networks with Applications to Model Reference Adaptive Control

no code implementations • 25 Mar 2024 • Andrew Lamperski, Tyler Lekang

Neural networks are regularly employed in adaptive control of nonlinear systems and related methods of reinforcement learning.

Function Approximation with Randomly Initialized Neural Networks for Approximate Model Reference Adaptive Control

no code implementations • 28 Mar 2023 • 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 on the activation function.

Simple Algorithms for Dueling Bandits

no code implementations • 18 Jun 2019 • Tyler Lekang, Andrew Lamperski

In this paper, we present simple algorithms for Dueling Bandits.