| A Theoretical Analysis of Contrastive Unsupervised Representation Learning S Arora, H Khandeparkar, M Khodak, O Plevrakis, N Saunshi International Conference on Machine Learning (ICML) 2019, 2019 | 914* | 2019 |
| A Large Self-Annotated Corpus for Sarcasm M Khodak, N Saunshi, K Vodrahalli Language Resources and Evaluation Conference (LREC) 2018, 2017 | 294 | 2017 |
| Predicting What You Already Know Helps: Provable Self-Supervised Learning JD Lee, Q Lei, N Saunshi, J Zhuo Neural Information Processing Systems (NeurIPS) 2021, 2020 | 213 | 2020 |
| A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors M Khodak*, N Saunshi*, Y Liang, T Ma, B Stewart, S Arora Association for Computational Linguistics (ACL) 2018, 2018 | 136 | 2018 |
| Understanding Contrastive Learning Requires Incorporating Inductive Biases N Saunshi, J Ash, S Goel, D Misra, C Zhang, S Arora, S Kakade, ... International Conference on Machine Learning (ICML) 2022, 2022 | 131 | 2022 |
| A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks N Saunshi, S Malladi, S Arora International Conference on Learning Representations (ICLR) 2021, 2020 | 91 | 2020 |
| Provable representation learning for imitation learning via bi-level optimization S Arora, S Du, S Kakade, Y Luo, N Saunshi International Conference on Machine Learning (ICML) 2020, 2020 | 79 | 2020 |
| Task-Specific Skill Localization in Fine-tuned Language Models A Panigrahi*, N Saunshi*, H Zhao, S Arora International Conference on Machine Learning (ICML) 2023, 2023 | 76 | 2023 |
| A compressed sensing view of unsupervised text embeddings, bag-of-n-grams, and LSTMs S Arora, M Khodak, N Saunshi, K Vodrahalli International Conference on Learning Representations (ICLR) 2018, 2018 | 54 | 2018 |
| Reasoning in Large Language Models Through Symbolic Math Word Problems V Gaur, N Saunshi Findings of the Association for Computational Linguistics (ACL) 2023, 2023 | 31* | 2023 |
| Understanding Influence Functions and Datamodels via Harmonic Analysis N Saunshi, A Gupta, M Braverman, S Arora International Conference on Learning Representations (ICLR) 2023, 2022 | 27 | 2022 |
| A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning N Saunshi, A Gupta, W Hu International Conference on Machine Learning (ICML) 2021, 2021 | 27 | 2021 |
| A sample complexity separation between non-convex and convex meta-learning N Saunshi, Y Zhang, M Khodak, S Arora International Conference on Machine Learning (ICML) 2020, 2020 | 24 | 2020 |
| Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? K Gatmiry, N Saunshi, SJ Reddi, S Jegelka, S Kumar Forty-first International Conference on Machine Learning, 2024 | 17 | 2024 |
| On Predicting Generalization using GANs Y Zhang, A Gupta, N Saunshi, S Arora International Conference on Learning Representations (ICLR) 2022, 2021 | 14 | 2021 |
| Pixie: a social chatbot O Adewale, A Beatson, D Buniatyan, J Ge, M Khodak, H Lee, N Prasad, ... Alexa prize proceedings, 2017 | 13 | 2017 |
| Efficient Stagewise Pretraining via Progressive Subnetworks A Panigrahi*, N Saunshi*, K Lyu, S Miryoosefi, S Reddi, S Kale, S Kumar International Conference on Learning Representations (ICLR) 2025, 2024 | 7 | 2024 |
| New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound A Gupta*, N Saunshi*, D Yu*, K Lyu, S Arora Neural Information Processing Systems (NeurIPS) 2022, 2021 | 7 | 2021 |
| On the Role of Depth and Looping for In-Context Learning with Task Diversity K Gatmiry, N Saunshi, SJ Reddi, S Jegelka, S Kumar arXiv preprint arXiv:2410.21698, 2024 | 2 | 2024 |
| A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs AS Rawat, V Sadhanala, A Rostamizadeh, A Chakrabarti, W Jitkrittum, ... arXiv preprint arXiv:2410.18779, 2024 | 2 | 2024 |
Mikhail KhodakCMU在 cs.princeton.edu 的电子邮件经过验证