465 citations · 1.1k across the 28 of their papers we have counts for
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Peering Through Preferences: Unraveling Feedback Acquisition for Aligning Large Language Models
Hritik Bansal, John Dang, Aditya Grover
Aligning large language models (LLMs) with human values and intents critically involves the use of human or AI feedback. While dense feedback annotations are expensive to acquire a…
ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling
Tung Nguyen, Jason Jewik, Hritik Bansal +2
Modeling weather and climate is an essential endeavor to understand the near- and long-term impacts of climate change, as well as inform technology and policymaking for adaptation…
Diffusion Models for Black-Box Optimization
Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover
The goal of offline black-box optimization (BBO) is to optimize an expensive black-box function using a fixed dataset of function evaluations. Prior works consider forward approach…
Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models
Siyan Zhao, Aditya Grover
Reinforcement learning presents an attractive paradigm to reason about several distinct aspects of sequential decision making, such as specifying complex goals, planning future obs…
Scaling Pareto-Efficient Decision Making Via Offline Multi-Objective RL
Baiting Zhu, Meihua Dang, Aditya Grover
The goal of multi-objective reinforcement learning (MORL) is to learn policies that simultaneously optimize multiple competing objectives. In practice, an agent's preferences over…
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning
Hritik Bansal, Nishad Singhi, Yu Yang +3
Multimodal contrastive pretraining has been used to train multimodal representation models, such as CLIP, on large amounts of paired image-text data. However, previous studies have…