5 citations · 6 across the 4 of their papers we have counts for
8 papers
Beyond Regret for Decentralized Bandits in Matching Markets
Soumya Basu, Karthik Abinav Sankararaman, Abishek Sankararaman
We design decentralized algorithms for regret minimization in the two-sided matching market with one-sided bandit feedback that significantly improves upon the prior works (Liu et…
On Generalization of Adaptive Methods for Over-parameterized Linear Regression
Vatsal Shah, Soumya Basu, Anastasios Kyrillidis +1
Over-parameterization and adaptive methods have played a crucial role in the success of deep learning in the last decade. The widespread use of over-parameterization has forced us…
Stochastic Linear Bandits with Protected Subspace
Advait Parulekar, Soumya Basu, Aditya Gopalan +2
We study a variant of the stochastic linear bandit problem wherein we optimize a linear objective function but rewards are accrued only orthogonal to an unknown subspace (which we…
Dominate or Delete: Decentralized Competing Bandits in Serial Dictatorship
Abishek Sankararaman, Soumya Basu, Karthik Abinav Sankararaman
Online learning in a two-sided matching market, with demand side agents continuously competing to be matched with supply side (arms), abstracts the complex interactions under parti…
Contextual Blocking Bandits
Soumya Basu, Orestis Papadigenopoulos, Constantine Caramanis +1
We study a novel variant of the multi-armed bandit problem, where at each time step, the player observes an independently sampled context that determines the arms' mean rewards. Ho…
Learning Mixtures of Graphs from Epidemic Cascades
Jessica Hoffmann, Soumya Basu, Surbhi Goel +1
We consider the problem of learning the weighted edges of a balanced mixture of two undirected graphs from epidemic cascades. While mixture models are popular modeling tools, algor…