4 citations · 4 across the 5 of their papers we have counts for
5 papers
Towards Provable Log Density Policy Gradient
Pulkit Katdare, Anant Joshi, Katherine Driggs-Campbell
Policy gradient methods are a vital ingredient behind the success of modern reinforcement learning. Modern policy gradient methods, although successful, introduce a residual error…
Marginalized Importance Sampling for Off-Environment Policy Evaluation
Pulkit Katdare, Nan Jiang, Katherine Driggs-Campbell
Reinforcement Learning (RL) methods are typically sample-inefficient, making it challenging to train and deploy RL-policies in real world robots. Even a robust policy trained in si…
Efficient Equivariant Transfer Learning from Pretrained Models
Sourya Basu, Pulkit Katdare, Prasanna Sattigeri +4
Efficient transfer learning algorithms are key to the success of foundation models on diverse downstream tasks even with limited data. Recent works of Basu et al. (2023) and Kaba e…
Off Environment Evaluation Using Convex Risk Minimization
Pulkit Katdare, Shuijing Liu, Katherine Driggs-Campbell
Applying reinforcement learning (RL) methods on robots typically involves training a policy in simulation and deploying it on a robot in the real world. Because of the model mismat…
Trajectory Tracking Using Motion Primitives for the Purcell's Swimmer
Sudin Kadam, Kedar Joshi, Naman Gupta +2
Locomotion at low Reynolds numbers is a topic of growing interest, spurred by its various engineering and medical applications. This paper presents a novel prototype and a locomoti…