Search Results for author:
Found 5 papers, 0 papers with code
Dis-entangling Mixture of Interventions on a Causal Bayesian Network Using Aggregate Observations
We study the problem of separating a mixture of distributions, all of which come from interventions on a known causal bayesian network.
A Causal Bandit Approach to Learning Good Atomic Interventions in Presence of Unobserved Confounders
First, we propose a simple regret minimization algorithm that takes as input a semi-Markovian causal graph with atomic interventions and possibly unobservable variables, and achieves $\tilde{O}(\sqrt{M/T})$ expected simple regret, where $M$ is dependent on the input CBN and could be very small compared to the number of arms.
Intervention Efficient Algorithm for Two-Stage Causal MDPs
We study Markov Decision Processes (MDP) wherein states correspond to causal graphs that stochastically generate rewards.
Offsetting Unequal Competition through RL-assisted Incentive Schemes
On inspecting, we realize that an overall incentive scheme for the weak team does not incentivize the weaker agents within that team to learn and improve.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraint
Following the discovery of segments, delivery of messages to users through preferred media channels like Facebook and Google can be challenging, as only a portion of users in a behavior segment find match in a medium, and only a fraction of those matched actually see the message (exposure).
