Kaustubh Ponkshe

According to our database, Kaustubh Ponkshe authored at least 9 papers between 2024 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

ABBA: Highly Expressive Hadamard Product Adaptation for Large Language Models.

Raghav Singhal, Kaustubh Ponkshe, Rohit Vartak, Praneeth Vepakomma

CoRR, May, 2025

Safety Subspaces are Not Distinct: A Fine-Tuning Case Study.

Kaustubh Ponkshe, Shaan Shah, Raghav Singhal, Praneeth Vepakomma

CoRR, May, 2025

Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning.

Raghav Singhal, Kaustubh Ponkshe, Rohit Vartak, Lav R. Varshney, Praneeth Vepakomma

CoRR, February, 2025

A Lightweight Method to Disrupt Memorized Sequences in LLM.

Parjanya Prashant, Kaustubh Ponkshe, Babak Salimi

CoRR, February, 2025

GuideQ: Framework for Guided Questioning for progressive informational collection and classification.

Priya Mishra, Suraj Racha, Kaustubh Ponkshe, Adit Akarsh, Ganesh Ramakrishnan

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2025, Albuquerque, New Mexico, USA, April 29, 2025

FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Large Language Models.

Raghav Singhal, Kaustubh Ponkshe, Praneeth Vepakomma

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024

Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning.

Kaustubh Ponkshe, Raghav Singhal, Eduard Gorbunov, Alexey Tumanov, Samuel Horváth, Praneeth Vepakomma

CoRR, 2024

StructFormer: Document Structure-based Masked Attention and its Impact on Language Model Pre-Training.

Kaustubh Ponkshe, Venkatapathy Subramanian, Natwar Modani, Ganesh Ramakrishnan

CoRR, 2024

Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models.

Raghav Singhal, Kaustubh Ponkshe, Praneeth Vepakomma

CoRR, 2024