5 citations · 13 across the 20 of their papers we have counts for
20 papers
Reducing Oracle Feedback with Vision-Language Embeddings for Preference-Based RL
Udita Ghosh, Dripta S. Raychaudhuri, Jiachen Li +2
Preference-based reinforcement learning can learn effective reward functions from comparisons, but its scalability is constrained by the high cost of oracle feedback. Lightweight v…
Towards Source-Free Machine Unlearning
Sk Miraj Ahmed, Umit Yigit Basaran, Dripta S. Raychaudhuri +5
As machine learning becomes more pervasive and data privacy regulations evolve, the ability to remove private or copyrighted information from trained models is becoming an increasi…
Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning
Udita Ghosh, Dripta S. Raychaudhuri, Jiachen Li +2
Preference-based reinforcement learning (RL) offers a promising approach for aligning policies with human intent but is often constrained by the high cost of human feedback. In thi…
Multi-modal Pose Diffuser: A Multimodal Generative Conditional Pose Prior
Calvin-Khang Ta, Arindam Dutta, Rohit Kundu +4
The Skinned Multi-Person Linear (SMPL) model plays a crucial role in 3D human pose estimation, providing a streamlined yet effective representation of the human body. However, ensu…
Robust Offline Imitation Learning from Diverse Auxiliary Data
Udita Ghosh, Dripta S. Raychaudhuri, Jiachen Li +2
Offline imitation learning enables learning a policy solely from a set of expert demonstrations, without any environment interaction. To alleviate the issue of distribution shift a…
Open-World Dynamic Prompt and Continual Visual Representation Learning
Youngeun Kim, Jun Fang, Qin Zhang +7
The open world is inherently dynamic, characterized by ever-evolving concepts and distributions. Continual learning (CL) in this dynamic open-world environment presents a significa…