| The power of depth for feedforward neural networks R Eldan, O Shamir Conference on learning theory, 907-940, 2016 | 673 | 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 | 114 | 2017 |
| Sampling from a log-concave distribution with projected langevin monte carlo S Bubeck, R Eldan, J Lehec Discrete & Computational Geometry 59 (4), 757-783, 2018 | 102 | 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 | 98 | 2013 |
| Testing for high‐dimensional geometry in random graphs S Bubeck, J Ding, R Eldan, MZ Rácz Random Structures & Algorithms 49 (3), 503-532, 2016 | 92 | 2016 |
| A two-sided estimate for the Gaussian noise stability deficit R Eldan Inventiones mathematicae 201 (2), 561-624, 2015 | 78 | 2015 |
| Multi-scale exploration of convex functions and bandit convex optimization S Bubeck, R Eldan Conference on Learning Theory, 583-589, 2016 | 68 | 2016 |
| Gaussian-width gradient complexity, reverse log-Sobolev inequalities and nonlinear large deviations R Eldan Geometric and Functional Analysis 28 (6), 1548-1596, 2018 | 64 | 2018 |
| Approximately gaussian marginals and the hyperplane conjecture R Eldan, B Klartag Concentration, functional inequalities and isoperimetry 545, 55-68, 2011 | 55 | 2011 |
| The entropic barrier: a simple and optimal universal self-concordant barrier S Bubeck, R Eldan arXiv preprint arXiv:1412.1587, 2014 | 46 | 2014 |
| Volumetric properties of the convex hull of an -dimensional Brownian motion R Eldan Electronic Journal of Probability 19, 1-34, 2014 | 37 | 2014 |
| Pointwise estimates for marginals of convex bodies R Eldan, B Klartag Journal of Functional Analysis 254 (8), 2275-2293, 2008 | 36 | 2008 |
| Finite-time analysis of projected Langevin Monte Carlo S Bubeck, R Eldan, J Lehec Advances in Neural Information Processing Systems 28, 2015 | 34 | 2015 |
| Bandit smooth convex optimization: Improving the bias-variance tradeoff O Dekel, R Eldan, T Koren Advances in Neural Information Processing Systems 28, 2015 | 33 | 2015 |
| 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 Bernoulli 23 (4A), 2887-2916, 2017 | 29 | 2017 |
| The CLT in high dimensions: quantitative bounds via martingale embedding R Eldan, D Mikulincer, A Zhai The Annals of Probability 48 (5), 2494-2524, 2020 | 24 | 2020 |
| On multiple peaks and moderate deviations for the supremum of a Gaussian field J Ding, R Eldan, A Zhai The Annals of Probability 43 (6), 3468-3493, 2015 | 24 | 2015 |
| Dimensionality and the stability of the Brunn-Minkowski inequality R Eldan, B Klartag arXiv preprint arXiv:1110.6584, 2011 | 24 | 2011 |
| Network size and weights size for memorization with two-layers neural networks S Bubeck, R Eldan, YT Lee, D Mikulincer arXiv preprint arXiv:2006.02855, 2020 | 19 | 2020 |
| Efficient algorithms for discrepancy minimization in convex sets R Eldan, M Singh Random Structures & Algorithms 53 (2), 289-307, 2018 | 19 | 2018 |
