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Matus Telgarsky
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Tensor decompositions for learning latent variable models
A Anandkumar, R Ge, D Hsu, SM Kakade, M Telgarsky
Journal of machine learning research 15, 2773-2832, 2014
12762014
Spectrally-normalized margin bounds for neural networks
PL Bartlett, DJ Foster, MJ Telgarsky
Advances in neural information processing systems 30, 2017
11362017
Benefits of depth in neural networks
M Telgarsky
Conference on learning theory, 1517-1539, 2016
6132016
Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis
M Raginsky, A Rakhlin, M Telgarsky
Conference on Learning Theory, 1674-1703, 2017
4942017
The implicit bias of gradient descent on nonseparable data
Z Ji, M Telgarsky
Conference on Learning Theory, 1772-1798, 2019
281*2019
Representation benefits of deep feedforward networks
M Telgarsky
arXiv preprint arXiv:1509.08101, 2015
2372015
Gradient descent aligns the layers of deep linear networks
Z Ji, M Telgarsky
arXiv preprint arXiv:1810.02032, 2018
1992018
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks
Z Ji, M Telgarsky
arXiv preprint arXiv:1909.12292, 2019
1682019
Directional convergence and alignment in deep learning
Z Ji, M Telgarsky
Advances in Neural Information Processing Systems 33, 17176-17186, 2020
1152020
Hartigan’s method: k-means clustering without voronoi
M Telgarsky, A Vattani
Proceedings of the thirteenth international conference on artificial …, 2010
1142010
Neural networks and rational functions
M Telgarsky
International Conference on Machine Learning, 3387-3393, 2017
822017
Margins, shrinkage, and boosting
M Telgarsky
International Conference on Machine Learning, 307-315, 2013
752013
Gradient descent follows the regularization path for general losses
Z Ji, M Dudík, RE Schapire, M Telgarsky
Conference on Learning Theory, 2109-2136, 2020
492020
Neural tangent kernels, transportation mappings, and universal approximation
Z Ji, M Telgarsky, R Xian
arXiv preprint arXiv:1910.06956, 2019
452019
Characterizing the implicit bias via a primal-dual analysis
Z Ji, M Telgarsky
Algorithmic Learning Theory, 772-804, 2021
412021
Agglomerative bregman clustering
M Telgarsky, S Dasgupta
arXiv preprint arXiv:1206.6446, 2012
402012
A Primal-Dual Convergence Analysis of Boosting.
M Telgarsky, Y Singer
Journal of Machine Learning Research 13 (3), 2012
332012
Early-stopped neural networks are consistent
Z Ji, J Li, M Telgarsky
Advances in Neural Information Processing Systems 34, 1805-1817, 2021
272021
Generalization bounds via distillation
D Hsu, Z Ji, M Telgarsky, L Wang
arXiv preprint arXiv:2104.05641, 2021
262021
Tensor decompositions for learning latent variable models (A survey for ALT)
A Anandkumar, R Ge, D Hsu, SM Kakade, M Telgarsky
Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff …, 2015
262015
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Artikelen 1–20