Artigos com autorizações de acesso público - Manfred K. WarmuthSaiba mais
Disponíveis em algum local: 25
The probably approximately correct (PAC) and other learning models
D Haussler, M Warmuth
The Mathematics of Generalization, 17-36, 2018
Autorizações: US Department of Defense
Robust bi-tempered logistic loss based on bregman divergences
E Amid, MKK Warmuth, R Anil, T Koren
Advances in Neural Information Processing Systems 32, 2019
Autorizações: US National Science Foundation
Leveraged volume sampling for linear regression
M Derezinski, MKK Warmuth, DJ Hsu
Advances in Neural Information Processing Systems 31, 2018
Autorizações: US National Science Foundation
Online PCA with optimal regret
J Nie, W Kotlowski, MK Warmuth
Journal of Machine Learning Research 17 (173), 1-49, 2016
Autorizações: US National Science Foundation
Unbiased estimates for linear regression via volume sampling
M Derezinski, MKK Warmuth
Advances in Neural Information Processing Systems 30, 2017
Autorizações: US National Science Foundation
Reverse iterative volume sampling for linear regression
M Dereziński, MK Warmuth
Journal of Machine Learning Research 19 (23), 1-39, 2018
Autorizações: US National Science Foundation
Reparameterizing mirror descent as gradient descent
E Amid, MKK Warmuth
Advances in Neural Information Processing Systems 33, 8430-8439, 2020
Autorizações: US National Science Foundation
Adaptive scale-invariant online algorithms for learning linear models
M Kempka, W Kotlowski, MK Warmuth
International conference on machine learning, 3321-3330, 2019
Autorizações: US National Science Foundation
Winnowing with gradient descent
E Amid, MK Warmuth
Conference on Learning Theory, 163-182, 2020
Autorizações: US National Science Foundation
Unlabeled sample compression schemes and corner peelings for ample and maximum classes
J Chalopin, V Chepoi, S Moran, MK Warmuth
Journal of Computer and System Sciences 127, 1-28, 2022
Autorizações: US National Science Foundation, Agence Nationale de la Recherche
Labeled compression schemes for extremal classes
S Moran, MK Warmuth
Algorithmic Learning Theory: 27th International Conference, ALT 2016, Bari …, 2016
Autorizações: US National Science Foundation
Minimax fixed-design linear regression
PL Bartlett, WM Koolen, A Malek, E Takimoto, MK Warmuth
Conference on Learning Theory, 226-239, 2015
Autorizações: Australian Research Council
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression
M Dereziński, KL Clarkson, MW Mahoney, MK Warmuth
Conference on Learning Theory, 1050-1069, 2019
Autorizações: US National Science Foundation, US Department of Defense
Unbiased estimators for random design regression
M Dereziński, MK Warmuth, D Hsu
Journal of Machine Learning Research 23 (167), 1-46, 2022
Autorizações: US National Science Foundation
Correcting the bias in least squares regression with volume-rescaled sampling
M Derezinski, MK Warmuth, D Hsu
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Autorizações: US National Science Foundation
Kernelization of matrix updates, when and how?
MK Warmuth, W Kotłowski, S Zhou
Theoretical Computer Science 558, 159-178, 2014
Autorizações: National Natural Science Foundation of China
Online dynamic programming
H Rahmanian, MKK Warmuth
Advances in Neural Information Processing Systems 30, 2017
Autorizações: US National Science Foundation
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint
E Amid, MK Warmuth
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3179-3186, 2020
Autorizações: US National Science Foundation
Rank-smoothed pairwise learning in perceptual quality assessment
H Talebi, E Amid, P Milanfar, MK Warmuth
2020 IEEE International Conference on Image Processing (ICIP), 3413-3417, 2020
Autorizações: US National Science Foundation
Divergence-based motivation for online EM and combining hidden variable models
E Amid, MK Warmuth
Conference on Uncertainty in Artificial Intelligence, 81-90, 2020
Autorizações: US National Science Foundation
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