2 citations · 4 across the 9 of their papers we have counts for
9 papers
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…
Disentangling Mixtures of Unknown Causal Interventions
Abhinav Kumar, Gaurav Sinha
In many real-world scenarios, such as gene knockout experiments, targeted interventions are often accompanied by unknown interventions at off-target sites. Moreover, different unit…
Universal Lower Bound for Learning Causal DAGs with Atomic Interventions
Vibhor Porwal, Piyush Srivastava, Gaurav Sinha
A well-studied challenge that arises in the structure learning problem of causal directed acyclic graphs (DAG) is that using observational data, one can only learn the graph up to…
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…
A Causal Bandit Approach to Learning Good Atomic Interventions in Presence of Unobserved Confounders
Aurghya Maiti, Vineet Nair, Gaurav Sinha
We study the problem of determining the best intervention in a Causal Bayesian Network (CBN) specified only by its causal graph. We model this as a stochastic multi-armed bandit (M…
Efficient reconstruction of depth three circuits with top fan-in two
Gaurav Sinha
We develop efficient randomized algorithms to solve the black-box reconstruction problem for polynomials over finite fields, computable by depth three arithmetic circuits with alte…
Budgeted and Non-budgeted Causal Bandits
Vineet Nair, Vishakha Patil, Gaurav Sinha
Learning good interventions in a causal graph can be modelled as a stochastic multi-armed bandit problem with side-information. First, we study this problem when interventions are…
Dis-entangling Mixture of Interventions on a Causal Bayesian Network Using Aggregate Observations
Gaurav Sinha, Ayush Chauhan, Aurghya Maiti +2
We study the problem of separating a mixture of distributions, all of which come from interventions on a known causal bayesian network. Given oracle access to marginals of all dist…
Reconstruction of depth-3, top fan-in two circuits over characteristic zero fields
Gaurav Sinha
Reconstruction of arithmetic circuits has been heavily studied in the past few years and has connections to proving lower bounds and deterministic identity testing. In this paper w…