12 citations · 53 across the 19 of their papers we have counts for
6 papers · 1 filter
Learning Good Interventions in Causal Graphs via Covering
Ayush Sawarni, Rahul Madhavan, Gaurav Sinha +1
We study the causal bandit problem that entails identifying a near-optimal intervention from a specified set of (possibly non-atomic) interventions over a given causal graph. H…
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits
Siddharth Barman, Arindam Khan, Arnab Maiti +1
We extend the notion of regret with a welfarist perspective. Focussing on the classic multi-armed bandit (MAB) framework, the current work quantifies the performance of bandit algo…
Intervention Efficient Algorithm for Two-Stage Causal MDPs
Rahul Madhavan, Aurghya Maiti, Gaurav Sinha +1
We study Markov Decision Processes (MDP) wherein states correspond to causal graphs that stochastically generate rewards. In this setup, the learner's goal is to identify atomic in…
Optimal Algorithms for Range Searching over Multi-Armed Bandits
Siddharth Barman, Ramakrishnan Krishnamurthy, Saladi Rahul
This paper studies a multi-armed bandit (MAB) version of the range-searching problem. In its basic form, range searching considers as input a set of points (on the real line) and a…
Online Learning for Structured Loss Spaces
Siddharth Barman, Aditya Gopalan, Aadirupa Saha
We consider prediction with expert advice when the loss vectors are assumed to lie in a set described by the sum of atomic norm balls. We derive a regret bound for a general versio…
Online Convex Optimization Using Predictions
Niangjun Chen, Anish Agarwal, Adam Wierman +2
Making use of predictions is a crucial, but under-explored, area of online algorithms. This paper studies a class of online optimization problems where we have external noisy predi…