4 citations · 4 across the 10 of their papers we have counts for
11 papers
Minibatch Selection for Language Models via Partition Matroid Constrained Gradient Matching
Prayas Agrawal, Prateek Chanda, Ishita Khatri +3
Training large language models (LLMs) on heterogeneous data requires selecting minibatches that balance convergence speed with coverage across domains. Existing methods either sele…
Online Distributional Prediction via Latent Cluster Geometry Under Drift and Corruption
Navyansh Mahla, Prateek Chanda, Ganesh Ramakrishnan
Online learning in non-stationary streams is often formulated as tracking a point estimate, although many applications require predicting the full data-generating distribution. We…
UniPROT: Uniform Prototype Selection via Partial Optimal Transport with Submodular Guarantees
Prateek Chanda, Prayas Agrawal, Karthik S. Gurumoorthy +3
Selecting prototypical examples from a source distribution to represent a target data distribution is a fundamental problem in machine learning. Existing subset selection methods o…
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…
Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning
Prateek Chanda, Saral Sureka, Parth Pratim Chatterjee +3
Supervised fine-tuning performance for large language models depends strongly on how training budget is distributed across a heterogeneous set of tasks. In practice, mixtures are o…
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…
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
Saeel Sandeep Nachane, Ojas Gramopadhye, Prateek Chanda +5
In this paper, we propose a modified version of the MedQA-USMLE dataset, named MEDQA-OPEN, which contains open-ended medical questions without options to mimic clinical scenarios,…