6 papers
Bandit Guided Submodular Curriculum for Adaptive Subset Selection
Prateek Chanda, Prayas Agrawal, Saral Sureka +3
Traditional curriculum learning proceeds from easy to hard samples, yet defining a reliable notion of difficulty remains elusive. Prior work has used submodular functions to induce…
Bayesian Coreset Optimization for Personalized Federated Learning
Prateek Chanda, Shrey Modi, Ganesh Ramakrishnan
In a distributed machine learning setting like Federated Learning where there are multiple clients involved which update their individual weights to a single central server, often…
PARAM-1 BharatGen 2.9B Model
Kundeshwar Pundalik, Piyush Sawarkar, Nihar Sahoo +19
Large Language Models (LLMs) have emerged as powerful general-purpose reasoning systems, yet their development remains dominated by English-centric data, architectures, and optimiz…
FairPO: Robust Preference Optimization for Fair Multi-Label Learning
Soumen Kumar Mondal, Prateek Chanda, Akshit Varmora +1
Multi-label classification (MLC) often suffers from performance disparities across labels. We propose \textbf{FairPO}, a framework combining preference-based loss and group-robust…
Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning
Nikhil Shivakumar Nayak, Krishnateja Killamsetty, Ligong Han +8
Continual learning in large language models (LLMs) is prone to catastrophic forgetting, where adapting to new tasks significantly degrades performance on previously learned ones. E…
Early Exit and Multi Stage Knowledge Distillation in VLMs for Video Summarization
Anas Anwarul Haq Khan, Utkarsh Verma, Ganesh Ramakrishnan
We introduce DEEVISum (Distilled Early Exit Vision language model for Summarization), a lightweight, efficient, and scalable vision language model designed for segment wise video s…