29 citations · 180 across the 55 of their papers we have counts for
51 papers · 1 filter
Fine-Tuning Diffusion Models via Intermediate Distribution Shaping
Gautham Govind Anil, Shaan Ul Haque, Nithish Kannen +3
Diffusion models are widely used for generative tasks across domains. Given a pre-trained diffusion model, it is often desirable to fine-tune it further either to correct for error…
A Personalized Exercise Assistant using Reinforcement Learning (PEARL): Results from a four-arm Randomized-controlled Trial
Amy Armento Lee, Narayan Hegde, Nina Deliu +16
Consistent physical inactivity poses a major global health challenge. Mobile health (mHealth) interventions, particularly Just-in-Time Adaptive Interventions (JITAIs), offer a prom…
Regret minimization in Linear Bandits with offline data via extended D-optimal exploration
Sushant Vijayan, Arun Suggala, Karthikeyan Shanmugam +1
We consider the problem of online regret minimization in linear bandits with access to prior observations (offline data) from the underlying bandit model. There are numerous applic…
Efficient Approximate Posterior Sampling with Annealed Langevin Monte Carlo
Advait Parulekar, Litu Rout, Karthikeyan Shanmugam +1
We study the problem of posterior sampling in the context of score based generative models. We have a trained score network for a prior , a measurement model , and ar…
Robust Reward Modeling via Causal Rubrics
Pragya Srivastava, Harman Singh, Rahul Madhavan +9
Reward models (RMs) are fundamental to aligning Large Language Models (LLMs) via human feedback, yet they often suffer from reward hacking. They tend to latch on to superficial or…
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions
Isha Puri, Amit Dhurandhar, Tejaswini Pedapati +3
In recent years there has been a considerable amount of research on local post hoc explanations for neural networks. However, work on building interpretable neural architectures ha…