| Sparks of artificial general intelligence: Early experiments with gpt-4 S Bubeck, V Chandrasekaran, R Eldan, J Gehrke, E Horvitz, E Kamar, ... arXiv preprint arXiv:2303.12712, 2023 | 2815 | 2023 |
| Path finding methods for linear programming: Solving linear programs in o (vrank) iterations and faster algorithms for maximum flow YT Lee, A Sidford 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, 424-433, 2014 | 444* | 2014 |
| Solving linear programs in the current matrix multiplication time MB Cohen, YT Lee, Z Song Journal of the ACM (JACM) 68 (1), 1-39, 2021 | 423 | 2021 |
| Textbooks are all you need S Gunasekar, Y Zhang, J Aneja, CCT Mendes, A Del Giorno, S Gopi, ... arXiv preprint arXiv:2306.11644, 2023 | 365 | 2023 |
| Optimal algorithms for smooth and strongly convex distributed optimization in networks K Scaman, F Bach, S Bubeck, YT Lee, L Massoulié international conference on machine learning, 3027-3036, 2017 | 363 | 2017 |
| A faster cutting plane method and its implications for combinatorial and convex optimization YT Lee, A Sidford, SC Wong 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 1049-1065, 2015 | 350 | 2015 |
| An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations JA Kelner, YT Lee, L Orecchia, A Sidford Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete …, 2014 | 329 | 2014 |
| Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv S Bubeck, V Chandrasekaran, R Eldan, J Gehrke, E Horvitz, E Kamar, ... arXiv preprint arXiv:2303.12712, 2023 | 308 | 2023 |
| Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems YT Lee, A Sidford 2013 ieee 54th annual symposium on foundations of computer science, 147-156, 2013 | 284 | 2013 |
| Differentially private fine-tuning of language models D Yu, S Naik, A Backurs, S Gopi, HA Inan, G Kamath, J Kulkarni, YT Lee, ... arXiv preprint arXiv:2110.06500, 2021 | 269 | 2021 |
| 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 | 261 | 2023 |
| Uniform sampling for matrix approximation MB Cohen, YT Lee, C Musco, C Musco, R Peng, A Sidford Proceedings of the 2015 conference on innovations in theoretical computer …, 2015 | 253 | 2015 |
| Adversarial examples from computational constraints S Bubeck, YT Lee, E Price, I Razenshteyn International Conference on Machine Learning, 831-840, 2019 | 245 | 2019 |
| 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 | 237 | 2024 |
| Geometric median in nearly linear time MB Cohen, YT Lee, G Miller, J Pachocki, A Sidford Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016 | 198 | 2016 |
| A geometric alternative to Nesterov's accelerated gradient descent S Bubeck, YT Lee, M Singh arXiv preprint arXiv:1506.08187, 2015 | 191 | 2015 |
| Optimal algorithms for non-smooth distributed optimization in networks K Scaman, F Bach, S Bubeck, L Massoulié, YT Lee Advances in Neural Information Processing Systems 31, 2018 | 189 | 2018 |
| Kernel-based methods for bandit convex optimization S Bubeck, R Eldan, YT Lee Journal of the ACM (JACM) 68 (4), 1-35, 2021 | 185 | 2021 |
| Can generalist foundation models outcompete special-purpose tuning? case study in medicine H Nori, YT Lee, S Zhang, D Carignan, R Edgar, N Fusi, N King, J Larson, ... arXiv preprint arXiv:2311.16452, 2023 | 182 | 2023 |
| Efficient Inverse Maintenance and Faster Algorithms for Linear Programming YT Lee, A Sidford arXiv preprint arXiv:1503.01752, 2015 | 178 | 2015 |
Sebastien BubeckVP AI and Distinguished Scientist, Microsoft確認したメール アドレス: microsoft.com
