| A latent variable model approach to pmi-based word embeddings S Arora, Y Li, Y Liang, T Ma, A Risteski Transactions of the Association for Computational Linguistics 4, 385-399, 2016 | 490 | 2016 |
| Linear algebraic structure of word senses, with applications to polysemy S Arora, Y Li, Y Liang, T Ma, A Risteski Transactions of the Association of Computational Linguistics 6, 483-495, 2018 | 234 | 2018 |
| The Risks of Invariant Risk Minimization E Rosenfeld, P Ravikumar, A Risteski International Conference on Learning Representations (ICLR), 2020, 2020 | 232 | 2020 |
| Do GANs learn the distribution? some theory and empirics S Arora, A Risteski, Y Zhang International Conference on Learning Representations (ICLR), 2019, 2018 | 163 | 2018 |
| On the ability of neural nets to express distributions H Lee, R Ge, T Ma, A Risteski, S Arora Conference on Learning Theory, 1271-1296, 2017 | 85 | 2017 |
| Approximability of Discriminators Implies Diversity in GANs Y Bai, T Ma, A Risteski International Conference on Learning Representations (ICLR), 2020, 2018 | 75 | 2018 |
| Random walks on context spaces: Towards an explanation of the mysteries of semantic word embeddings S Arora, Y Li, Y Liang, T Ma, A Risteski arXiv preprint arXiv:1502.03520, 385-399, 2015 | 62 | 2015 |
| A latent variable model approach to PMI-based word embeddings S Arora, Y Li, Y Liang, T Ma, A Risteski arXiv preprint arXiv:1502.03520, 2015 | 58 | 2015 |
| Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization E Rosenfeld, P Ravikumar, A Risteski arXiv preprint arXiv:2202.06856, 2022 | 49 | 2022 |
| Automated WordNet Construction Using Word Embeddings M Khodak, A Risteski, C Fellbaum, S Arora Proceedings of the 1st Workshop on Sense, Concept and Entity Representations …, 2017 | 48* | 2017 |
| Beyond log-concavity: Provable guarantees for sampling multi-modal distributions using simulated tempering langevin monte carlo H Lee, A Risteski, R Ge Advances in neural information processing systems 31, 7847-7856, 2018 | 43* | 2018 |
| Recovery guarantee of weighted low-rank approximation via alternating minimization Y Li, Y Liang, A Risteski International Conference on Machine Learning, 2358-2367, 2016 | 36 | 2016 |
| Algorithms and matching lower bounds for approximately-convex optimization A Risteski, Y Li Advances in Neural Information Processing Systems 29, 4745-4753, 2016 | 36 | 2016 |
| Provable learning of noisy-or networks S Arora, R Ge, T Ma, A Risteski Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017 | 34 | 2017 |
| Recovery guarantee of non-negative matrix factorization via alternating updates Y Li, Y Liang, A Risteski Advances in Neural Information Processing Systems, 4987-4995, 2016 | 33 | 2016 |
| Empirical study of the benefits of overparameterization in learning latent variable models RD Buhai, Y Halpern, Y Kim, A Risteski, D Sontag International Conference on Machine Learning, 1211-1219, 2020 | 29* | 2020 |
| Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective V Jain, F Koehler, A Risteski Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019 | 28 | 2019 |
| An online learning approach to interpolation and extrapolation in domain generalization E Rosenfeld, P Ravikumar, A Risteski International Conference on Artificial Intelligence and Statistics, 2641-2657, 2022 | 26 | 2022 |
| The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure F Koehler, A Risteski International Conference on Learning Representations, 2018 | 25* | 2018 |
| Statistical Efficiency of Score Matching: The View from Isoperimetry F Koehler, A Heckett, A Risteski arXiv preprint arXiv:2210.00726, 2022 | 24 | 2022 |
Sanjeev AroraProfessor of Computer Science, Princeton UniversityGeverifieerd e-mailadres voor cs.princeton.edu
