| The Implicit Bias of Gradient Descent on Separable Data D Soudry, E Hoffer, MS Nacson, S Gunasekar, N Srebro The Journal of Machine Learning Research, 2018 | 1048 | 2018 |
| Phi-3 technical report: A highly capable language model locally on your phone M Abdin, SA Jacobs, AA Awan, J Aneja, A Awadallah, H Awadalla, ... arXiv preprint arXiv:2404.14219, 2024 | 723 | 2024 |
| Implicit regularization in matrix factorization S Gunasekar, BE Woodworth, S Bhojanapalli, B Neyshabur, N Srebro Advances in Neural Information Processing Systems, 6151-6159, 2017 | 568 | 2017 |
| Textbooks Are All You Need S Gunasekar, Y Zhang, J Aneja, CCT Mendes, A Del Giorno, S Gopi, ... arXiv preprint arXiv:2306.11644, 2023 | 537 | 2023 |
| Characterizing Implicit Bias in Terms of Optimization Geometry S Gunasekar, J Lee, D Soudry, N Srebro Proceedings of the 35th International Conference on Machine Learning 80 …, 2018 | 492 | 2018 |
| Implicit bias of gradient descent on linear convolutional networks S Gunasekar, JD Lee, D Soudry, N Srebro Advances in Neural Information Processing Systems, 9461-9471, 2018 | 469 | 2018 |
| Learning Non-Discriminatory Predictors B Woodworth, S Gunasekar, MI Ohannessian, N Srebro Proceedings of the 2017 Conference on Learning Theory (COLT) 35, 2017 | 459 | 2017 |
| Kernel and rich regimes in overparametrized models B Woodworth, S Gunasekar, JD Lee, E Moroshko, P Savarese, I Golan, ... Conference on Learning Theory, 3635-3673, 2020 | 411 | 2020 |
| Textbooks Are All You Need II: phi-1.5 technical report Y Li, S Bubeck, R Eldan, A Del Giorno, S Gunasekar, YT Lee arXiv preprint arXiv:2309.05463, 2023 | 391 | 2023 |
| Convergence of gradient descent on separable data MS Nacson, J Lee, S Gunasekar, PHP Savarese, N Srebro, D Soudry Proceedings of the 22nd International Conference on Artificial Intelligence …, 2019 | 180 | 2019 |
| Implicit bias in deep linear classification: Initialization scale vs training accuracy E Moroshko, BE Woodworth, S Gunasekar, JD Lee, N Srebro, D Soudry Advances in neural information processing systems 33, 22182-22193, 2020 | 95 | 2020 |
| Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning Y Jiang, P Natekar, M Sharma, SK Aithal, D Kashyap, N Subramanyam, ... NeurIPS 2020 Competition and Demonstration Track, 170-190, 2021 | 86* | 2021 |
| Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models MS Nacson, S Gunasekar, JD Lee, N Srebro, D Soudry Proceedings of the 36 th International Conference on Machine Learning 97, 2019 | 81 | 2019 |
| Data Augmentation as Feature Manipulation R Shen, S Bubeck, S Gunasekar International Conference on Machine Learning, 19773-19808, 2022 | 72 | 2022 |
| Review quality aware collaborative filtering S Raghavan, S Gunasekar, J Ghosh Proceedings of the sixth ACM conference on Recommender systems, 123-130, 2012 | 72 | 2012 |
| Unveiling Transformers with LEGO: a synthetic reasoning task Y Zhang, A Backurs, S Bubeck, R Eldan, S Gunasekar, T Wagner arXiv preprint arXiv:2206.04301, 2022 | 70 | 2022 |
| Exponential family matrix completion under structural constraints S Gunasekar, P Ravikumar, J Ghosh International Conference on Machine Learning, 1917-1925, 2014 | 60 | 2014 |
| Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent S Gunasekar, B Woodworth, N Srebro International Conference on Artificial Intelligence and Statistics, 2305-2313, 2021 | 48* | 2021 |
| Noisy matrix completion using alternating minimization S Gunasekar, A Acharya, N Gaur, J Ghosh Joint European conference on machine learning and knowledge discovery in …, 2013 | 45 | 2013 |
| Face detection on distorted images augmented by perceptual quality-aware features S Gunasekar, J Ghosh, AC Bovik IEEE transactions on information forensics and security 9 (12), 2119-2131, 2014 | 40 | 2014 |
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu