5k citations · 5k across the 17 of their papers we have counts for
17 papers
Bilevel Optimization of Synthetic Trajectories for Multi-Turn LLM Fine-Tuning
Shresth Verma, Mauricio Tec, Cheol Woo Kim +2
While LLMs excel at single-turn generation, they struggle with long-horizon, multi-turn interactions. Offline reinforcement learning (RL) offers a scalable approach, yet its perfor…
Decisions and Deployment: The Five-Year SAHELI Project (2020-2025) on Restless Multi-Armed Bandits for Improving Maternal and Child Health
Shresth Verma, Arpan Dasgupta, Neha Madhiwalla +2
Maternal and child health is a critical concern around the world. In many global health programs disseminating preventive care and health information, limited healthcare worker res…
Lightweight Robust Direct Preference Optimization
Cheol Woo Kim, Shresth Verma, Mauricio Tec +1
Direct Preference Optimization (DPO) has become a popular method for fine-tuning large language models (LLMs) due to its stability and simplicity. However, it is also known to be s…
Preference Robustness for DPO with Applications to Public Health
Cheol Woo Kim, Shresth Verma, Mauricio Tec +1
We study an LLM fine-tuning task for designing reward functions for sequential resource allocation problems in public health, guided by human preferences expressed in natural langu…
Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning
Cheol Woo Kim, Jai Moondra, Shresth Verma +4
In many real-world applications of reinforcement learning (RL), deployed policies have varied impacts on different stakeholders, creating challenges in reaching consensus on how to…
Measuring Fairness in Financial Transaction Machine Learning Models
Deniz Sezin Ayvaz, Lorenzo Belenguer, Hankun He +12
Mastercard, a global leader in financial services, develops and deploys machine learning models aimed at optimizing card usage and preventing attrition through advanced predictive…
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards
Shresth Verma, Niclas Boehmer, Lingkai Kong +1
LLMs are increasingly used to design reward functions based on human preferences in Reinforcement Learning (RL). We focus on LLM-designed rewards for Restless Multi-Armed Bandits,…
Improving Health Information Access in the World's Largest Maternal Mobile Health Program via Bandit Algorithms
Arshika Lalan, Shresth Verma, Paula Rodriguez Diaz +6
Harnessing the wide-spread availability of cell phones, many nonprofits have launched mobile health (mHealth) programs to deliver information via voice or text to beneficiaries in…
Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation
Arshika Lalan, Shresth Verma, Kumar Madhu Sudan +4
Mobile health programs are becoming an increasingly popular medium for dissemination of health information among beneficiaries in less privileged communities. Kilkari is one of the…
Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcare
Panayiotis Danassis, Shresth Verma, Jackson A. Killian +2
The success of many healthcare programs depends on participants' adherence. We consider the problem of scheduling interventions in low resource settings (e.g., placing timely suppo…
Decision-Focused Evaluation: Analyzing Performance of Deployed Restless Multi-Arm Bandits
Paritosh Verma, Shresth Verma, Aditya Mate +2
Restless multi-arm bandits (RMABs) is a popular decision-theoretic framework that has been used to model real-world sequential decision making problems in public health, wildlife c…
The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
The Astropy Collaboration, Adrian M. Price-Whelan, Pey Lian Lim +133
The Astropy Project supports and fosters the development of open-source and openly-developed Python packages that provide commonly needed functionality to the astronomical communit…
Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health
Kai Wang, Shresth Verma, Aditya Mate +5
This paper studies restless multi-armed bandit (RMAB) problems with unknown arm transition dynamics but with known correlated arm features. The goal is to learn a model to predict…
Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health
Aditya Mate, Lovish Madaan, Aparna Taneja +6
The widespread availability of cell phones has enabled non-profits to deliver critical health information to their beneficiaries in a timely manner. This paper describes our work t…
Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots
Shresth Verma, Haritha S. Nair, Gaurav Agarwal +2
Robotics has proved to be an indispensable tool in many industrial as well as social applications, such as warehouse automation, manufacturing, disaster robotics, etc. In most of t…
IIITM Face: A Database for Facial Attribute Detection in Constrained and Simulated Unconstrained Environments
Raj Kuwar Gupta, Shresth Verma, KV Arya +2
This paper addresses the challenges of face attribute detection specifically in the Indian context. While there are numerous face datasets in unconstrained environments, none of th…
Emergence of Writing Systems Through Multi-Agent Cooperation
Shresth Verma, Joydip Dhar
Learning to communicate is considered an essential task to develop a general AI. While recent literature in language evolution has studied emergent language through discrete or con…