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05/20/2023
A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks
We present a novel algorithm for training deep neural networks in superv...
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05/11/2023
Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays
We generalize the Borkar-Meyn stability Theorem (BMT) to distributed sto...
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01/27/2022
Distributed gradient-based optimization in the presence of dependent aperiodic communication
Iterative distributed optimization algorithms involve multiple agents th...
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01/03/2022
Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms
We present sufficient conditions that ensure convergence of the multi-ag...
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05/10/2021
Practical sufficient conditions for convergence of distributed optimisation algorithms over communication networks with interference
Information exchange over networks can be affected by various forms of d...
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08/25/2020
Theory of Deep Q-Learning: A Dynamical Systems Perspective
Deep Q-Learning is an important algorithm, used to solve sequential deci...
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05/15/2019
Deep reinforcement learning for scheduling in large-scale networked control systems
This work considers the problem of control and resource scheduling in ne...
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03/17/2019
DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme
In this paper, we present an asynchronous approximate gradient method th...
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10/15/2018
Multi-Stage Reinforcement Learning For Object Detection
We present a reinforcement learning approach for detecting objects withi...
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03/08/2018
DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling
We consider networked control systems consisting of multiple independent...
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02/22/2018
Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning
Asynchronous stochastic approximations are an important class of model-f...
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09/14/2017
Conditions for Stability and Convergence of Set-Valued Stochastic Approximations: Applications to Approximate Value and Fixed point Iterations
The main aim of this paper is the development of easily verifiable suffi...
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04/01/2016
Analysis of gradient descent methods with non-diminishing, bounded errors
The main aim of this paper is to provide an analysis of gradient descent...
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04/23/2015
Stability of Stochastic Approximations with `Controlled Markov' Noise and Temporal Difference Learning
In this paper we present a `stability theorem' for stochastic approximat...
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02/06/2015
Stochastic recursive inclusion in two timescales with an application to the Lagrangian dual problem
In this paper we present a framework to analyze the asymptotic behavior ...
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02/06/2015