37 citations · 62 across the 14 of their papers we have counts for
10 papers · 1 filter
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming
Alec Koppel, Amrit Singh Bedi, Bhargav Ganguly +1
In tabular multi-agent reinforcement learning with average-cost criterion, a team of agents sequentially interacts with the environment and observes local incentives. We focus on t…
Online Trajectory Optimization Using Inexact Gradient Feedback for Time-Varying Environments
Mohan Krishna Nutalapati, Amrit Singh Bedi, Ketan Rajawat +1
This paper considers the problem of online trajectory design under time-varying environments. We formulate the general trajectory optimization problem within the framework of time-…
Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony
Amrit Singh Bedi, Alec Koppel, Ketan Rajawat +1
An open challenge in supervised learning is \emph{conceptual drift}: a data point begins as classified according to one label, but over time the notion of that label changes. Beyon…
Online Learning over Dynamic Graphs via Distributed Proximal Gradient Algorithm
Rishabh Dixit, Amrit Singh Bedi, Ketan Rajawat
We consider the problem of tracking the minimum of a time-varying convex optimization problem over a dynamic graph. Motivated by target tracking and parameter estimation problems i…
Escaping Saddle Points with the Successive Convex Approximation Algorithm
Amrit Singh Bedi, Ketan Rajawat, Vaneet Aggarwal
Optimizing non-convex functions is of primary importance in the vast majority of machine learning algorithms. Even though many gradient descent based algorithms have been studied,…
Nonparametric Compositional Stochastic Optimization for Risk-Sensitive Kernel Learning
Amrit Singh Bedi, Alec Koppel, Ketan Rajawat +1
In this work, we address optimization problems where the objective function is a nonlinear function of an expected value, i.e., compositional stochastic {strongly convex programs}.…