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ORCIDNikunj Saunshi
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2020 – today
- 2024
[c16]Khashayar Gatmiry, Nikunj Saunshi, Sashank J. Reddi, Stefanie Jegelka, Sanjiv Kumar:
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? ICML 2024- [i20]Abhishek Panigrahi, Nikunj Saunshi, Kaifeng Lyu, Sobhan Miryoosefi, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
Efficient Stagewise Pretraining via Progressive Subnetworks. CoRR abs/2402.05913 (2024) - [i19]Stefani Karp, Nikunj Saunshi, Sobhan Miryoosefi, Sashank J. Reddi, Sanjiv Kumar:
Landscape-Aware Growing: The Power of a Little LAG. CoRR abs/2406.02469 (2024) - [i18]Nikunj Saunshi, Stefani Karp, Shankar Krishnan, Sobhan Miryoosefi, Sashank J. Reddi, Sanjiv Kumar:
On the Inductive Bias of Stacking Towards Improving Reasoning. CoRR abs/2409.19044 (2024) - [i17]Khashayar Gatmiry, Nikunj Saunshi, Sashank J. Reddi, Stefanie Jegelka, Sanjiv Kumar:
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? CoRR abs/2410.08292 (2024) - [i16]Ankit Singh Rawat, Veeranjaneyulu Sadhanala, Afshin Rostamizadeh, Ayan Chakrabarti, Wittawat Jitkrittum, Vladimir Feinberg, Seungyeon Kim, Hrayr Harutyunyan, Nikunj Saunshi, Zachary Nado, Rakesh Shivanna, Sashank J. Reddi, Aditya Krishna Menon, Rohan Anil, Sanjiv Kumar:
A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs. CoRR abs/2410.18779 (2024) - [i15]Khashayar Gatmiry, Nikunj Saunshi, Sashank J. Reddi, Stefanie Jegelka, Sanjiv Kumar:
On the Role of Depth and Looping for In-Context Learning with Task Diversity. CoRR abs/2410.21698 (2024) - 2023
- [c15]Vedant Gaur, Nikunj Saunshi:
Reasoning in Large Language Models Through Symbolic Math Word Problems. ACL (Findings) 2023: 5889-5903 - [c14]Nikunj Saunshi, Arushi Gupta, Mark Braverman, Sanjeev Arora:
Understanding Influence Functions and Datamodels via Harmonic Analysis. ICLR 2023 - [c13]Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao, Sanjeev Arora:
Task-Specific Skill Localization in Fine-tuned Language Models. ICML 2023: 27011-27033 - [i14]Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao, Sanjeev Arora:
Task-Specific Skill Localization in Fine-tuned Language Models. CoRR abs/2302.06600 (2023) - [i13]Vedant Gaur, Nikunj Saunshi:
Reasoning in Large Language Models Through Symbolic Math Word Problems. CoRR abs/2308.01906 (2023) - 2022
- [c12]Yi Zhang, Arushi Gupta, Nikunj Saunshi, Sanjeev Arora:
On Predicting Generalization using GANs. ICLR 2022 - [c11]Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy:
Understanding Contrastive Learning Requires Incorporating Inductive Biases. ICML 2022: 19250-19286 - [c10]Arushi Gupta, Nikunj Saunshi, Dingli Yu, Kaifeng Lyu, Sanjeev Arora:
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. NeurIPS 2022 - [i12]Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy:
Understanding Contrastive Learning Requires Incorporating Inductive Biases. CoRR abs/2202.14037 (2022) - [i11]Nikunj Saunshi, Arushi Gupta, Mark Braverman, Sanjeev Arora:
Understanding Influence Functions and Datamodels via Harmonic Analysis. CoRR abs/2210.01072 (2022) - [i10]Arushi Gupta, Nikunj Saunshi, Dingli Yu, Kaifeng Lyu, Sanjeev Arora:
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound. CoRR abs/2211.02912 (2022) - 2021
- [c9]Nikunj Saunshi, Sadhika Malladi, Sanjeev Arora:
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks. ICLR 2021 - [c8]Nikunj Saunshi, Arushi Gupta, Wei Hu:
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning. ICML 2021: 9333-9343 - [c7]Jason D. Lee, Qi Lei, Nikunj Saunshi, Jiacheng Zhuo:
Predicting What You Already Know Helps: Provable Self-Supervised Learning. NeurIPS 2021: 309-323 - [i9]Nikunj Saunshi, Arushi Gupta, Wei Hu:
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning. CoRR abs/2106.15615 (2021) - [i8]Yi Zhang, Arushi Gupta, Nikunj Saunshi, Sanjeev Arora:
On Predicting Generalization using GANs. CoRR abs/2111.14212 (2021) - 2020
- [c6]Sanjeev Arora, Simon S. Du, Sham M. Kakade, Yuping Luo, Nikunj Saunshi:
Provable Representation Learning for Imitation Learning via Bi-level Optimization. ICML 2020: 367-376 - [c5]Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora:
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. ICML 2020: 8512-8521 - [i7]Sanjeev Arora, Simon S. Du, Sham M. Kakade, Yuping Luo, Nikunj Saunshi:
Provable Representation Learning for Imitation Learning via Bi-level Optimization. CoRR abs/2002.10544 (2020) - [i6]Nikunj Saunshi, Yi Zhang, Mikhail Khodak, Sanjeev Arora:
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning. CoRR abs/2002.11172 (2020) - [i5]Jason D. Lee, Qi Lei, Nikunj Saunshi, Jiacheng Zhuo:
Predicting What You Already Know Helps: Provable Self-Supervised Learning. CoRR abs/2008.01064 (2020) - [i4]Nikunj Saunshi, Sadhika Malladi, Sanjeev Arora:
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks. CoRR abs/2010.03648 (2020)
2010 – 2019
- 2019
- [c4]Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, Hrishikesh Khandeparkar:
A Theoretical Analysis of Contrastive Unsupervised Representation Learning. ICML 2019: 5628-5637 - [i3]Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, Nikunj Saunshi:
A Theoretical Analysis of Contrastive Unsupervised Representation Learning. CoRR abs/1902.09229 (2019) - 2018
- [c3]Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora:
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. ACL (1) 2018: 12-22 - [c2]Sanjeev Arora, Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli:
A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs. ICLR (Poster) 2018 - [c1]Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli:
A Large Self-Annotated Corpus for Sarcasm. LREC 2018 - [i2]Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora:
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. CoRR abs/1805.05388 (2018) - 2017
- [i1]Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli:
A Large Self-Annotated Corpus for Sarcasm. CoRR abs/1704.05579 (2017)
Coauthor Index
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