no code implementations • 4 Feb 2024 • Harshita Chopra, Atanu R. Sinha, Sunav Choudhary, Ryan A. Rossi, Paavan Kumar Indela, Veda Pranav Parwatala, Srinjayee Paul, Aurghya Maiti
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).
no code implementations • 5 Jan 2022 • Paramita Koley, Aurghya Maiti, Sourangshu Bhattacharya, Niloy Ganguly
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
no code implementations • 1 Nov 2021 • Rahul Madhavan, Aurghya Maiti, Gaurav Sinha, Siddharth Barman
We study Markov Decision Processes (MDP) wherein states correspond to causal graphs that stochastically generate rewards.
no code implementations • 6 Jul 2021 • Aurghya Maiti, Vineet Nair, Gaurav Sinha
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.
no code implementations • 30 Nov 2019 • Gaurav Sinha, Ayush Chauhan, Aurghya Maiti, Naman Poddar, Pulkit Goel
We study the problem of separating a mixture of distributions, all of which come from interventions on a known causal bayesian network.