1 citations · 1 across the 5 of their papers we have counts for
5 papers
Packing Linear Programs and Fractional Knapsack using Comparison Oracles
Ritabrata Barat, Siddharth Barman, Nirjhar Das +1
We study the problem of recovering the objective of a packing linear program when the algorithm accesses only comparison information about optimal solutions under varying constrain…
Welfarist Formulations for Diverse Similarity Search
Siddharth Barman, Nirjhar Das, Shivam Gupta +1
Nearest Neighbor Search (NNS) is a fundamental problem in data structures with wide-ranging applications, such as web search, recommendation systems, and, more recently, retrieval-…
Causal Contextual Bandits with Adaptive Context
Rahul Madhavan, Aurghya Maiti, Gaurav Sinha +1
We study a variant of causal contextual bandits where the context is chosen based on an initial intervention chosen by the learner. At the beginning of each round, the learner sele…
Generalized Linear Bandits with Limited Adaptivity
Ayush Sawarni, Nirjhar Das, Siddharth Barman +1
We study the generalized linear contextual bandit problem within the constraints of limited adaptivity. In this paper, we present two algorithms, and $\texttt{R…
Nash Regret Guarantees for Linear Bandits
Ayush Sawarni, Soumybrata Pal, Siddharth Barman
We obtain essentially tight upper bounds for a strengthened notion of regret in the stochastic linear bandits framework. The strengthening -- referred to as Nash regret -- is defin…