5 citations · 12 across the 6 of their papers we have counts for
6 papers
CAPSULE: Control-Theoretic Action Perturbations for Safe Uncertainty-Aware Reinforcement Learning
Rahul Narava, Siddharth Verma, Ojas Jain +2
Ensuring safe exploration in high-dimensional systems with unknown dynamics remains a significant challenge. Existing safe reinforcement learning methods often provide safety guara…
OPT-R: Exploring the Role of Explanations in Finetuning and Prompting for Reasoning Skills of Large Language Models
Badr AlKhamissi, Siddharth Verma, Ping Yu +3
In this paper, we conduct a thorough investigation into the reasoning capabilities of Large Language Models (LLMs), focusing specifically on the Open Pretrained Transformers (OPT)…
Uniform Masking Prevails in Vision-Language Pretraining
Siddharth Verma, Yuchen Lu, Rui Hou +4
Masked Language Modeling (MLM) has proven to be an essential component of Vision-Language (VL) pretraining. To implement MLM, the researcher must make two design choices: the maski…
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning
Siddharth Verma, Justin Fu, Mengjiao Yang +1
Conventionally, generation of natural language for dialogue agents may be viewed as a statistical learning problem: determine the patterns in human-provided data and generate appro…
Continual Learning of Control Primitives: Skill Discovery via Reset-Games
Kelvin Xu, Siddharth Verma, Chelsea Finn +1
Reinforcement learning has the potential to automate the acquisition of behavior in complex settings, but in order for it to be successfully deployed, a number of practical challen…
Fast Online "Next Best Offers" using Deep Learning
Rekha Singhal, Gautam Shroff, Mukund Kumar +5
In this paper, we present iPrescribe, a scalable low-latency architecture for recommending 'next-best-offers' in an online setting. The paper presents the design of iPrescribe and…