5 citations · 14 across the 6 of their papers we have counts for
6 papers · 1 filter
Generalization Properties of Retrieval-based Models
Soumya Basu, Ankit Singh Rawat, Manzil Zaheer
Many modern high-performing machine learning models such as GPT-3 primarily rely on scaling up models, e.g., transformer networks. Simultaneously, a parallel line of work aims to i…
Combinatorial Blocking Bandits with Stochastic Delays
Alexia Atsidakou, Orestis Papadigenopoulos, Soumya Basu +2
Recent work has considered natural variations of the multi-armed bandit problem, where the reward distribution of each arm is a special function of the time passed since its last p…
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…
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…