Publications

  1. Suhail Mohmad, V. S. Borkar, “ Mean Field Games through local interactions”, in Advances in Dynamic and Mean Field Games, Birkhauser, 2017, pp. 131-141.

  2. Suhail Mohmad, V. S. Borkar, “Q-learning for Markov decision processes with a satisfiability criterion”, Systems and Control Letters, Volume 113, Pages 45-51, 2018.

  3. Suhail Mohmad, V. S. Borkar, “Distributed Stochastic Approximation with local Projections”, SIAM Journal of Optimization, Volume 28, issue 4,.3375-3401, 2018.

  4. Suhail Mohmad, “Stochastic Approximation on Riemannian manifolds”, Applied Mathematics and Optimization, Volume 79, Pages 1-29, 2019.

  5. Suhail Mohmad, K. Avrachenkov, Vivek Borkar, S. Moharir, “Dynamic Social Learning under graphical consraints”, in IEEE Transactions on control of network systems, Pages 1435-1446, 2021.

  6. Suhail Mohmad, V. K. Lau “Model Compression for communication efficient Federated Learning”, IEEE Transactions on Neural Networks and Learning Systems, 2021.

  7. Suhail Mohmad, Liqun Su, V. K. Lau “Robust Federated Learning over Noisy Fading Channels”, to appear in IEEE Internet of Things Journal, 2022.

  8. Suhail Mohmad, V. K. Lau, “ Gradient based Augmented Lagrangian methods for constrained optimization”, conditionally accepted in IEEE Transactions on Signal Processing, 2022.


CONFERENCE PUBLICATIONS


  1. V. S. Borkar, S. M. Shah, “ Distributed Algorithms: Tsitsiklis and Beyond”, Workshop on Information Theory and Applications, San Diego, Feb.11-16, 2018.


SUBMITTED WORKS/UNDER PREPARATION


  1. Suhail Mohmad, “ Distributed Optimization on Riemannian Manifolds for multi-agent networks”, under revision, SIAM Journal of Optimization.

  2. Suhail Mohmad, “Making Simulated Annealing Efficient for Bandit and Stochasctic Opitmizaion”, under review Applied Mathematics and Optimization.

  3. Suhail Mohmad, R. Bollapragada “Optimal convergence rates for smooth, strongly convex distributed optimization with inexact communication”, under preparation.


INVITED TALKS


  1. “Distributed Stochastic Approximation with Projection”, presented at the Workshop on Applied Probability, Tata Institute of Fundemental research (TIFR), Mumbai, April 2017.

  2. “On ADMM and proximal Methods for modern large scale optimzaition’, IIT-Bombay, Feb. 2020