activity
20232026
most citedTIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Alignment-Aware Model Adaptation via Feedback-Guided Optimization

Gaurav Bhatt, Aditya Chinchure, Jiawei Zhou +1

Fine-tuning is the primary mechanism for adapting foundation models to downstream tasks; however, standard approaches largely optimize task objectives in isolation and do not accou…

cs.IR2025

RewardRank: Optimizing True Learning-to-Rank Utility

Gaurav Bhatt, Kiran Koshy Thekumparampil, Tanmay Gangwani +2

Traditional ranking systems optimize offline proxy objectives that rely on oversimplified assumptions about user behavior, often neglecting factors such as position bias and item d…

cs.CV2024

Preventing Catastrophic Forgetting through Memory Networks in Continuous Detection

Gaurav Bhatt, James Ross, Leonid Sigal

Modern pre-trained architectures struggle to retain previous information while undergoing continuous fine-tuning on new tasks. Despite notable progress in continual classification,…

cs.CV2023★ 1 cited

TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models

Aditya Chinchure, Pushkar Shukla, Gaurav Bhatt +4

Text-to-Image (TTI) generative models have shown great progress in the past few years in terms of their ability to generate complex and high-quality imagery. At the same time, thes…

cs.LG2023

Mitigating the Effect of Incidental Correlations on Part-based Learning

Gaurav Bhatt, Deepayan Das, Leonid Sigal +1

Intelligent systems possess a crucial characteristic of breaking complicated problems into smaller reusable components or parts and adjusting to new tasks using these part represen…