| The power of depth for feedforward neural networks R Eldan, O Shamir Conference on learning theory, 907-940, 2016 | 807 | 2016 |
| Kernel-based methods for bandit convex optimization S Bubeck, YT Lee, R Eldan Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017 | 145 | 2017 |
| Sampling from a log-concave distribution with projected langevin monte carlo S Bubeck, R Eldan, J Lehec Discrete & Computational Geometry 59, 757-783, 2018 | 121 | 2018 |
| Testing for high‐dimensional geometry in random graphs S Bubeck, J Ding, R Eldan, MZ Rácz Random Structures & Algorithms 49 (3), 503-532, 2016 | 120 | 2016 |
| Thin shell implies spectral gap up to polylog via a stochastic localization scheme R Eldan Geometric and Functional Analysis 23 (2), 532-569, 2013 | 111 | 2013 |
| A two-sided estimate for the Gaussian noise stability deficit R Eldan Inventiones mathematicae 201, 561-624, 2015 | 79 | 2015 |
| Multi-scale exploration of convex functions and bandit convex optimization S Bubeck, R Eldan Conference on Learning Theory, 583-589, 2016 | 74 | 2016 |
| Gaussian-width gradient complexity, reverse log-Sobolev inequalities and nonlinear large deviations R Eldan Geometric and Functional Analysis 28, 1548-1596, 2018 | 71 | 2018 |
| Approximately gaussian marginals and the hyperplane conjecture R Eldan, B Klartag Concentration, functional inequalities and isoperimetry 545, 55-68, 2011 | 60 | 2011 |
| The entropic barrier: a simple and optimal universal self-concordant barrier S Bubeck, R Eldan arXiv preprint arXiv:1412.1587, 2014 | 54 | 2014 |
| Efficient algorithms for discrepancy minimization in convex sets R Eldan, M Singh Random Structures & Algorithms 53 (2), 289-307, 2018 | 40 | 2018 |
| Finite-time analysis of projected Langevin Monte Carlo S Bubeck, R Eldan, J Lehec Advances in Neural Information Processing Systems 28, 2015 | 40 | 2015 |
| Volumetric properties of the convex hull of an -dimensional Brownian motion R Eldan | 37 | 2014 |
| Pointwise estimates for marginals of convex bodies R Eldan, B Klartag Journal of Functional Analysis 254 (8), 2275-2293, 2008 | 37 | 2008 |
| Bandit smooth convex optimization: Improving the bias-variance tradeoff O Dekel, R Eldan, T Koren Advances in Neural Information Processing Systems 28, 2015 | 34 | 2015 |
| Stability of the logarithmic Sobolev inequality via the Föllmer process R Eldan, J Lehec, Y Shenfeld | 31 | 2020 |
| The CLT in high dimensions: quantitative bounds via martingale embedding R Eldan, D Mikulincer, A Zhai | 30 | 2020 |
| Network size and size of the weights in memorization with two-layers neural networks S Bubeck, R Eldan, YT Lee, D Mikulincer Advances in Neural Information Processing Systems 33, 4977-4986, 2020 | 30 | 2020 |
| From trees to seeds: on the inference of the seed from large trees in the uniform attachment model S Bubeck, R Eldan, E Mossel, MZ Rácz | 29 | 2017 |
| Depth separations in neural networks: what is actually being separated? I Safran, R Eldan, O Shamir Conference on Learning Theory, 2664-2666, 2019 | 27 | 2019 |
