Nihal Nayak

I am a Ph.D. candidate of Computer Science at Brown University. I work with Stephen Bach on learning with limited labeled data and zero-shot learning.

In summer 2022, I interned at ASAPP with Clemens Rosenbaum and Ethan R. Elenberg on estimating the quality of clusterings.

Before my Ph.D., I worked as a NLP Engineer at Stride.AI. I have also worked with Prof. H S Jamadagni at the Indian Institute of Science (IISc) as an Intern and then briefly as a Project Assistant.

CV (Updated in October, 2022)

Email : nnayak2 [at] cs [dot] brown [dot] edu

news

Apr 1, 2023	New pre-print out: Does CLIP bind concepts? Probing compositionality in large image models.
Jan 19, 2023	Our work on compositional soft prompting was accepted to ICLR 2023.
Oct 5, 2022	Excited to share new pre-print on evaluating clusterings with few labeled data points.
Jul 14, 2022	Our work ZSL-KG was accepted to TMLR.

selected publications

ICLR

Learning to Compose Soft Prompts for Compositional Zero-Shot Learning

Nihal V. Nayak, Peilin Yu, and Stephen H. Bach

In International Conference on Learning Representations (ICLR) 2023

Bib PDF Code

@inproceedings{csp:iclr23,
  author = {Nayak, Nihal V. and Yu, Peilin and Bach, Stephen H.},
  title = {Learning to Compose Soft Prompts for Compositional Zero-Shot Learning},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  abbr = {ICLR},
  bibtex_show = {true},
  selected = {true},
  pdf = {https://arxiv.org/abs/2204.03574},
  code = {https://github.com/BatsResearch/csp}
}

TMLR

Zero-Shot Learning with Common Sense Knowledge Graphs

Nihal V. Nayak, and Stephen H. Bach

Transactions on Machine Learning Research 2022

Bib PDF Code Project

@article{nayak:tmlr22,
  author = {Nayak, Nihal V. and Bach, Stephen H.},
  title = {Zero-Shot Learning with Common Sense Knowledge Graphs},
  year = {2022},
  pdf = {https://arxiv.org/abs/2006.10713},
  project = {https://github.com/BatsResearch/zsl-kg},
  code = {https://github.com/BatsResearch/nayak-arxiv20-code},
  journal = {Transactions on Machine Learning Research},
  abbr = {TMLR},
  bibtex_show = {true},
  selected = {true}
}

ICLR

Multitask Prompted Training Enables Zero-Shot Task Generalization

Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush

In International Conference on Learning Representations (ICLR) 2022

Bib PDF Code Project

@inproceedings{sanh:iclr22,
  author = {Sanh, Victor and Webson, Albert and Raffel, Colin and Bach, Stephen H. and Sutawika, Lintang and Alyafeai, Zaid and Chaffin, Antoine and Stiegler, Arnaud and Scao, Teven Le and Raja, Arun and Dey, Manan and Bari, M Saiful and Xu, Canwen and Thakker, Urmish and Sharma, Shanya Sharma and Szczechla, Eliza and Kim, Taewoon and Chhablani, Gunjan and Nayak, Nihal V. and Datta, Debajyoti and Chang, Jonathan and Jiang, Mike Tian-Jian and Wang, Han and Manica, Matteo and Shen, Sheng and Yong, Zheng Xin and Pandey, Harshit and Bawden, Rachel and Wang, Thomas and Neeraj, Trishala and Rozen, Jos and Sharma, Abheesht and Santilli, Andrea and Fevry, Thibault and Fries, Jason Alan and Teehan, Ryan and Biderman, Stella and Gao, Leo and Bers, Tali and Wolf, Thomas and Rush, Alexander M.},
  title = {Multitask Prompted Training Enables Zero-Shot Task Generalization},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  pdf = {https://arxiv.org/abs/2110.08207},
  project = {https://github.com/bigscience-workshop/promptsource},
  code = {https://github.com/bigscience-workshop/t-zero},
  abbr = {ICLR},
  bibtex_show = {true},
  selected = {true}
}