4 papers
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks
Akshayaa Magesh, Venugopal V. Veeravalli
We consider a multi-player multi-armed bandit setting in the presence of adversaries that attempt to negatively affect the rewards received by the players in the system. The reward…
Autoequivariant Network Search via Group Decomposition
Sourya Basu, Akshayaa Magesh, Harshit Yadav +1
Recent works show that group equivariance as an inductive bias improves neural network performance for both classification and generation. However, designing group-equivariant neur…
Dynamic Spectrum Access using Stochastic Multi-User Bandits
Meghana Bande, Akshayaa Magesh, Venugopal V. Veeravalli
A stochastic multi-user multi-armed bandit framework is used to develop algorithms for uncoordinated spectrum access. In contrast to prior work, it is assumed that rewards can be n…
Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Access
Akshayaa Magesh, Venugopal V. Veeravalli
Multi-user multi-armed bandits have emerged as a good model for uncoordinated spectrum access problems. In this paper we consider the scenario where users cannot communicate with e…