| 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 | 602 | 2016 |
| The Risks of Invariant Risk Minimization E Rosenfeld, P Ravikumar, A Risteski International Conference on Learning Representations (ICLR), 2020, 2020 | 309 | 2020 |
| 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 | 270 | 2018 |
| Do GANs learn the distribution? some theory and empirics S Arora, A Risteski, Y Zhang International Conference on Learning Representations (ICLR), 2019, 2018 | 183 | 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 | 103 | 2017 |
| Approximability of Discriminators Implies Diversity in GANs Y Bai, T Ma, A Risteski International Conference on Learning Representations (ICLR), 2020, 2018 | 87 | 2018 |
| 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 | 80 | 2022 |
| 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 | 66 | 2015 |
| How do transformers learn topic structure: Towards a mechanistic understanding Y Li, Y Li, A Risteski International Conference on Machine Learning, 19689-19729, 2023 | 62 | 2023 |
| 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 | 58* | 2018 |
| 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 | 54* | 2017 |
| Statistical Efficiency of Score Matching: The View from Isoperimetry F Koehler, A Heckett, A Risteski arXiv preprint arXiv:2210.00726, 2022 | 49 | 2022 |
| Algorithms and matching lower bounds for approximately-convex optimization A Risteski, Y Li Advances in Neural Information Processing Systems 29, 4745-4753, 2016 | 42 | 2016 |
| 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 | 41 | 2019 |
| Recovery guarantee of weighted low-rank approximation via alternating minimization Y Li, Y Liang, A Risteski International Conference on Machine Learning, 2358-2367, 2016 | 39 | 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 | 35 | 2017 |
| Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments Y Chen, E Rosenfeld, M Sellke, T Ma, A Risteski Conference on Neural Information Processing Systems (NeurIPS), 2022, 2021 | 34 | 2021 |
| 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 | 34 | 2016 |
| Representational aspects of depth and conditioning in normalizing flows F Koehler, V Mehta, A Risteski International Conference on Machine Learning, 5628-5636, 2021 | 33 | 2021 |
| 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 | 33* | 2020 |
Sanjeev AroraProfessor of Computer Science, Princeton UniversityPotvrđena adresa e-pošte na cs.princeton.edu
