Stebėti
Andrej Risteski
Pavadinimas
Cituota
Cituota
Metai
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
5902016
The Risks of Invariant Risk Minimization
E Rosenfeld, P Ravikumar, A Risteski
International Conference on Learning Representations (ICLR), 2020, 2020
2712020
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
2492018
Do GANs learn the distribution? some theory and empirics
S Arora, A Risteski, Y Zhang
International Conference on Learning Representations (ICLR), 2019, 2018
1722018
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
942017
Approximability of Discriminators Implies Diversity in GANs
Y Bai, T Ma, A Risteski
International Conference on Learning Representations (ICLR), 2020, 2018
852018
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
652022
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
642015
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
50*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
49*2018
How do transformers learn topic structure: Towards a mechanistic understanding
Y Li, Y Li, A Risteski
International Conference on Machine Learning, 19689-19729, 2023
432023
Algorithms and matching lower bounds for approximately-convex optimization
A Risteski, Y Li
Advances in Neural Information Processing Systems 29, 4745-4753, 2016
392016
Statistical Efficiency of Score Matching: The View from Isoperimetry
F Koehler, A Heckett, A Risteski
arXiv preprint arXiv:2210.00726, 2022
382022
Recovery guarantee of weighted low-rank approximation via alternating minimization
Y Li, Y Liang, A Risteski
International Conference on Machine Learning, 2358-2367, 2016
372016
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
352019
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
342017
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
322016
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
292021
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
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
262022
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Straipsniai 1–20