1 citations · 1 across the 7 of their papers we have counts for
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Learning from Local Walks on Dynamic Graphs with Bandit Feedback
Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1
We study stochastic multi-armed bandits on dynamic graphs, where arms correspond to the vertices of a network with time-varying edges. In this setting, the learner is restricted to…
Dimension Agnostic Testing of Survey Data Credibility through the Lens of Regression
Debabrota Basu, Sourav Chakraborty, Debarshi Chanda +3
Assessing whether a sample survey credibly represents the population is a critical question for ensuring the validity of downstream research. Generally, this problem reduces to est…
Incentivized Lipschitz Bandits
Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1
We study incentivized exploration in multi-armed bandit (MAB) settings with infinitely many arms modeled as elements in continuous metric spaces. Unlike classical bandit models, we…
Testing Credibility of Public and Private Surveys through the Lens of Regression
Debabrota Basu, Sourav Chakraborty, Debarshi Chanda +3
Testing whether a sample survey is a credible representation of the population is an important question to ensure the validity of any downstream research. While this problem, in ge…
Incentivized Exploration of Non-Stationary Stochastic Bandits
Sourav Chakraborty, Lijun Chen
We study incentivized exploration for the multi-armed bandit (MAB) problem with non-stationary reward distributions, where players receive compensation for exploring arms other tha…