Publications

2025

1. Preprint

   Apertus: Democratizing Open and Compliant LLMs for Global Language Environments

   Apertus Team, and Kaustubh Ponkshe

   2025

   8B and 70B fully open-data, open-weights models, multilingual across 1000+ languages

   Bib HTML PDF

   @article{apertus,
     title = {Apertus: Democratizing Open and Compliant LLMs for Global Language Environments},
     author = {Team, Apertus and Ponkshe, Kaustubh},
     year = {2025},
     note = {8B and 70B fully open-data, open-weights models, multilingual across 1000+ languages},
   }

2. NeurIPS

   TokenSwap: A Lightweight Method to Disrupt Memorized Sequences in LLMs

   Parjanya Prashant^*, Kaustubh Ponkshe^*, and Babak Salimi

   2025

   NeurIPS 2025 (Spotlight)

   Bib HTML PDF Code

   @article{tokenswap,
     title = {TokenSwap: A Lightweight Method to Disrupt Memorized Sequences in LLMs},
     author = {Prashant, Parjanya and Ponkshe, Kaustubh and Salimi, Babak},
     year = {2025},
     note = {NeurIPS 2025 (Spotlight)},
   }

3. ACL

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

   Raghav Singhal^*, Kaustubh Ponkshe^*, and Praneeth Vepakomma

   2025

   ACL 2025 (Oral, Main Conference — top 2.1%)

   Bib PDF Code

   @article{fedex,
     title = {FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Large Language Models},
     author = {Singhal, Raghav and Ponkshe, Kaustubh and Vepakomma, Praneeth},
     year = {2025},
     note = {ACL 2025 (Oral, Main Conference — top 2.1%)},
     url = {https://raghavsinghal10.github.io/fedex-lora_page/},
   }

4. ICLR

   ABBA: Highly Expressive Hadamard Product Adaptation for Large Language Models

   Kaustubh Ponkshe^*, Raghav Singhal^*, Rohit Vartak^*, and 1 more author

   2025

   ICLR 2026; ES-FoMo @ ICML 2025 (Spotlight)

   Bib PDF Code

   @article{abba,
     title = {ABBA: Highly Expressive Hadamard Product Adaptation for Large Language Models},
     author = {Ponkshe, Kaustubh and Singhal, Raghav and Vartak, Rohit and Vepakomma, Praneeth},
     year = {2025},
     note = {ICLR 2026; ES-FoMo @ ICML 2025 (Spotlight)},
     url = {https://rohit01-zoey.github.io/abba-page/},
   }

5. ICLR

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

   Kaustubh Ponkshe^*, Shaan Shah^*, Raghav Singhal^*, and 1 more author

   2025

   ICLR 2026; INTERPLAY @ CoLM 2025

   Bib PDF Code

   @article{safety-subspaces,
     title = {Safety Subspaces are Not Distinct: A Fine-Tuning Case Study},
     author = {Ponkshe, Kaustubh and Shah, Shaan and Singhal, Raghav and Vepakomma, Praneeth},
     year = {2025},
     note = {ICLR 2026; INTERPLAY @ CoLM 2025},
   }

6. TMLR

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

   Raghav Singhal^*, Kaustubh Ponkshe^*, Rohit Vartak, and 2 more authors

   2025

   TMLR (J2C Certification — top 10% of accepted papers)

   Bib PDF Code

   @article{fed-sb,
     title = {Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning},
     author = {Singhal, Raghav and Ponkshe, Kaustubh and Vartak, Rohit and Varshney, Lav R. and Vepakomma, Praneeth},
     year = {2025},
     note = {TMLR (J2C Certification — top 10% of accepted papers)},
     url = {https://rohit01-zoey.github.io/fed-sb-page/},
   }

7. ICLR-W

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

   Kaustubh Ponkshe^*, Raghav Singhal^*, Eduard Gorbunov, and 3 more authors

   2025

   SCOPE @ ICLR 2025

   Bib PDF Code

   @article{lora-sb,
     title = {Initialization Using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning},
     author = {Ponkshe, Kaustubh and Singhal, Raghav and Gorbunov, Eduard and Tumanov, Alexey and Horvath, Samuel and Vepakomma, Praneeth},
     year = {2025},
     note = {SCOPE @ ICLR 2025},
     url = {https://raghavsinghal10.github.io/lora-sb-page/},
   }

8. NAACL

   GUIDEQ: Framework for Guided Questioning for Progressive Informational Collection and Classification

   Priya Mishra, Suraj Racha, Kaustubh Ponkshe, and 2 more authors

   2025

   NAACL 2025 Findings

   Bib PDF Code

   @article{guideq,
     title = {GUIDEQ: Framework for Guided Questioning for Progressive Informational Collection and Classification},
     author = {Mishra, Priya and Racha, Suraj and Ponkshe, Kaustubh and Akarsh, Adit and Ramakrishnan, Ganesh},
     year = {2025},
     note = {NAACL 2025 Findings},
   }

2024

1. AAAI-W

   StructFormer: Document Structure-Based Pre-training

   Kaustubh Ponkshe, Venkatapathy Subramanian, Ganesh Ramakrishnan, and 1 more author

   2024

   DocUI @ AAAI 2025

   Bib PDF Code

   @article{structformer,
     title = {StructFormer: Document Structure-Based Pre-training},
     author = {Ponkshe, Kaustubh and Subramanian, Venkatapathy and Ramakrishnan, Ganesh and Modani, Natwar},
     year = {2024},
     note = {DocUI @ AAAI 2025},
   }

2. CIKM-W

   Power Learning: Privacy-preserving Embeddings

   Kaustubh Ponkshe, Praneeth Vepakomma, and Peter Kairouz

   2024

   Trustworthy ML Workshop @ CIKM 2024 (Oral)

   Bib Code

   @article{power-learning,
     title = {Power Learning: Privacy-preserving Embeddings},
     author = {Ponkshe, Kaustubh and Vepakomma, Praneeth and Kairouz, Peter},
     year = {2024},
     note = {Trustworthy ML Workshop @ CIKM 2024 (Oral)},
   }

2021

1. ICIIP

   Crop Yield Prediction of Indian Districts Using Deep Learning

   Parjanya Prashant, Kaustubh Ponkshe, Chirag Garg, and 2 more authors

   2021

   ICIIP 2021

   Bib PDF Code

   @article{crop-yield,
     title = {Crop Yield Prediction of Indian Districts Using Deep Learning},
     author = {Prashant, Parjanya and Ponkshe, Kaustubh and Garg, Chirag and Pendse, Ishan and Muley, Prathamesh},
     year = {2021},
     note = {ICIIP 2021},
   }