3 citations · 7 across the 10 of their papers we have counts for
10 papers
Adaptive Federated Learning via Dynamical System Model
Aayushya Agarwal, Larry Pileggi, Gauri Joshi
Hyperparameter selection is critical for stable and efficient convergence of heterogeneous federated learning, where clients differ in computational capabilities, and data distribu…
Integrating Forecasting Models Within Steady-State Analysis and Optimization
Aayushya Agarwal, Larry Pileggi
Extreme weather variations and the increasing unpredictability of load behavior make it difficult to determine power grid dispatches that are robust to uncertainties. While machine…
A Hybrid Simulation of DNN-based Gray Box Models
Aayushya Agarwal, Yihan Ruan, Larry Pileggi
Simulation is vital for engineering disciplines, as it enables the prediction and design of physical systems. However, the computational challenges inherent to large-scale simulati…
FedECADO: A Dynamical System Model of Federated Learning
Aayushya Agarwal, Gauri Joshi, Larry Pileggi
Federated learning harnesses the power of distributed optimization to train a unified machine learning model across separate clients. However, heterogeneous data distributions and…
Second-Order Optimization via Quiescence
Aayushya Agarwal, Larry Pileggi, Ronald Rohrer
Second-order optimization methods exhibit fast convergence to critical points, however, in nonconvex optimization, these methods often require restrictive step-sizes to ensure a mo…
Towards Hyperparameter-Agnostic DNN Training via Dynamical System Insights
Carmel Fiscko, Aayushya Agarwal, Yihan Ruan +3
We present a stochastic first-order optimization method specialized for deep neural networks (DNNs), ECCO-DNN. This method models the optimization variable trajectory as a dynamica…
An Equivalent Circuit Approach to Distributed Optimization
Aayushya Agarwal, Larry Pileggi
Distributed optimization is an essential paradigm to solve large-scale optimization problems in modern applications where big-data and high-dimensionality creates a computational b…
An Equivalent Circuit Workflow for Unconstrained Optimization
Aayushya Agarwal, Carmel Fiscko, Soummya Kar +2
We introduce a new workflow for unconstrained optimization whereby objective functions are mapped onto a physical domain to more easily design algorithms that are robust to hyperpa…
ECCO: Equivalent Circuit Controlled Optimization
Aayushya Agarwal, Carmel Fiscko, Soummya Kar +2
We propose an adaptive optimization algorithm for solving unconstrained scaled gradient flow problems that achieves fast convergence by controlling the optimization trajectory shap…
Adversarially Robust Learning for Security-Constrained Optimal Power Flow
Priya L. Donti, Aayushya Agarwal, Neeraj Vijay Bedmutha +2
In recent years, the ML community has seen surges of interest in both adversarially robust learning and implicit layers, but connections between these two areas have seldom been ex…