no code implementations • 20 Jul 2022 • Adarsh M. Subramaniam, Akshayaa Magesh, Venugopal V. Veeravalli
Compressed Stochastic Gradient Descent (SGD) algorithms have been recently proposed to address the communication bottleneck in distributed and decentralized optimization problems, such as those that arise in federated machine learning.
no code implementations • 20 Jun 2022 • Akshayaa Magesh, Venugopal V. Veeravalli, Anirban Roy, Susmit Jha
While a number of tests for OOD detection have been proposed in prior work, a formal framework for studying this problem is lacking.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
1 code implementation • 10 Apr 2021 • Sourya Basu, Akshayaa Magesh, Harshit Yadav, Lav R. Varshney
We address these problems by proving a new group-theoretic result in the context of equivariant neural networks that shows that a network is equivariant to a large group if and only if it is equivariant to smaller groups from which it is constructed.
no code implementations • 12 Jan 2021 • Meghana Bande, Akshayaa Magesh, Venugopal V. Veeravalli
In contrast to prior work, it is assumed that rewards can be non-zero even under collisions, thus allowing for the number of users to be greater than the number of channels.
no code implementations • 21 Oct 2019 • Akshayaa Magesh, Venugopal V. Veeravalli
Multi-user multi-armed bandits have emerged as a good model for uncoordinated spectrum access problems.
no code implementations • 21 Oct 2019 • Akshayaa Magesh, Venugopal V. Veeravalli
Within this setup, where the number of players is allowed to be greater than the number of arms, we present a policy that achieves near order-optimal expected regret of order $O(\log^{1 + \delta} T)$ for some $0 < \delta < 1$ over a time-horizon of duration $T$.