5 citations · 18 across the 16 of their papers we have counts for
22 papers
Active Sensing Subserves Task-Level Control
Andrew Lamperski, Debojyoti Biswas, Eric S. Fortune +3
Active sensing is traditionally defined as the expenditure of energy, typically in the form of movement, for obtaining information. Here, we propose that the combination of relianc…
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…
An algorithm for bilevel optimization with traffic equilibrium constraints: convergence rate analysis
Akshit Goyal, Andrew Lamperski
Bilevel optimization with traffic equilibrium constraints plays an important role in transportation planning and management problems such as traffic control, transport network desi…
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…
Non-Asymptotic Pointwise and Worst-Case Bounds for Classical Spectrum Estimators
Andrew Lamperski
Spectrum estimation is a fundamental methodology in the analysis of time-series data, with applications including medicine, speech analysis, and control design. The asymptotic theo…
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…