28 citations · 30 across the 4 of their papers we have counts for
5 papers
Expert-Guided Rubrics as Dense Reward Signals for Reinforcement Learning in Non-Verifiable Domains
Ashish Bansal, Hossam Amer, Keerat Kaur Guliani +3
Large language models (LLMs) are increasingly deployed as autonomous agents in non-verifiable domains such as financial services, where they must interpret user intent, rewrite que…
De Jure: Iterative LLM Self-Refinement for Structured Extraction of Regulatory Rules
Keerat Guliani, Deepkamal Gill, David Landsman +3
Regulatory documents encode legally binding obligations that LLM-based systems must respect. Yet converting dense, hierarchically structured legal text into machine-readable rules…
DEAP Cache: Deep Eviction Admission and Prefetching for Cache
Ayush Mangal, Jitesh Jain, Keerat Kaur Guliani +1
Recent approaches for learning policies to improve caching, target just one out of the prefetching, admission and eviction processes. In contrast, we propose an end to end pipeline…
VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning
Raghav Awasthi, Keerat Kaur Guliani, Saif Ahmad Khan +7
A COVID-19 vaccine is our best bet for mitigating the ongoing onslaught of the pandemic. However, vaccine is also expected to be a limited resource. An optimal allocation strategy,…
Universal Adversarial Perturbations: A Survey
Ashutosh Chaubey, Nikhil Agrawal, Kavya Barnwal +2
Over the past decade, Deep Learning has emerged as a useful and efficient tool to solve a wide variety of complex learning problems ranging from image classification to human pose…