| 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 | 2886 | 2023 |
| The power of depth for feedforward neural networks R Eldan, O Shamir Conference on learning theory, 907-940, 2016 | 991 | 2016 |
| Textbooks are all you need S Gunasekar, Y Zhang, J Aneja, CCT Mendes, A Del Giorno, S Gopi, ... arXiv preprint arXiv:2306.11644, 2023 | 392 | 2023 |
| 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 | 317 | 2023 |
| 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 | 290 | 2024 |
| 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 | 281 | 2023 |
| Kernel-based methods for bandit convex optimization S Bubeck, R Eldan, YT Lee Journal of the ACM (JACM) 68 (4), 1-35, 2021 | 186 | 2021 |
| Testing for high‐dimensional geometry in random graphs S Bubeck, J Ding, R Eldan, MZ Rácz Random Structures & Algorithms 49 (3), 503-532, 2016 | 163 | 2016 |
| Sampling from a log-concave distribution with projected Langevin Monte Carlo S Bubeck, R Eldan, J Lehec Discrete & Computational Geometry 59, 757-783, 2018 | 155 | 2018 |
| Thin shell implies spectral gap up to polylog via a stochastic localization scheme R Eldan Geometric and Functional Analysis 23 (2), 532-569, 2013 | 151 | 2013 |
| Tinystories: How small can language models be and still speak coherent english? R Eldan, Y Li arXiv preprint arXiv:2305.07759, 2023 | 137 | 2023 |
| Phi-2: The surprising power of small language models M Javaheripi, S Bubeck, M Abdin, J Aneja, S Bubeck, CCT Mendes, ... Microsoft Research Blog, 2023 | 120 | 2023 |
| Who's Harry Potter? Approximate Unlearning in LLMs R Eldan, M Russinovich arXiv preprint arXiv:2310.02238, 2023 | 98 | 2023 |
| Gaussian-width gradient complexity, reverse log-Sobolev inequalities and nonlinear large deviations R Eldan Geometric and Functional Analysis 28 (6), 1548-1596, 2018 | 90 | 2018 |
| A two-sided estimate for the Gaussian noise stability deficit R Eldan Inventiones mathematicae 201, 561-624, 2015 | 85 | 2015 |
| Multi-scale exploration of convex functions and bandit convex optimization S Bubeck, R Eldan Conference on Learning Theory, 583-589, 2016 | 84 | 2016 |
| Localization schemes: A framework for proving mixing bounds for Markov chains Y Chen, R Eldan 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 76 | 2022 |
| Approximately gaussian marginals and the hyperplane conjecture R Eldan, B Klartag Concentration, functional inequalities and isoperimetry 545, 55-68, 2011 | 73 | 2011 |
| The entropic barrier: a simple and optimal universal self-concordant barrier S Bubeck, R Eldan arXiv preprint arXiv:1412.1587, 2014 | 66 | 2014 |
| A spectral condition for spectral gap: fast mixing in high-temperature Ising models R Eldan, F Koehler, O Zeitouni Probability theory and related fields 182 (3), 1035-1051, 2022 | 61 | 2022 |
