2 citations · 3 across the 5 of their papers we have counts for
5 papers
Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks
Gandharv Patil, Keyi Tang, Raquel Aoki +1
Individual treatment effects are not point-identified from data. The Probability of Necessity and Sufficiency (PNS) circumvents this limitation by characterizing individual-level c…
Robust Reward Modeling via Causal Rubrics
Pragya Srivastava, Harman Singh, Rahul Madhavan +9
Reward models (RMs) are fundamental to aligning Large Language Models (LLMs) via human feedback, yet they often suffer from reward hacking. They tend to latch on to superficial or…
On learning history based policies for controlling Markov decision processes
Gandharv Patil, Aditya Mahajan, Doina Precup
Reinforcementlearning(RL)folkloresuggeststhathistory-basedfunctionapproximationmethods,suchas recurrent neural nets or history-based state abstraction, perform better than their me…
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation
Gandharv Patil, Prashanth L. A., Dheeraj Nagaraj +1
We study the finite-time behaviour of the popular temporal difference (TD) learning algorithm when combined with tail-averaging. We derive finite time bounds on the parameter error…
Variance Penalized On-Policy and Off-Policy Actor-Critic
Arushi Jain, Gandharv Patil, Ayush Jain +2
Reinforcement learning algorithms are typically geared towards optimizing the expected return of an agent. However, in many practical applications, low variance in the return is de…