( en | de | ru )

 

Disambiguation

From post

копироватьудалитьдобавить публикацию в буфер
Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting.

, , , , , , и . ECML/PKDD (3), том 12459 из Lecture Notes in Computer Science, стр. 141-157. Springer, (2020)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed.

Emilie Morvant

Emilie Elsner

Emilie Kirch

Emilie Meiners

Emilie Kleindienst

 

Другие публикации лиц с тем же именем

VideoSense at TRECVID 2011: Semantic Indexing from Light Similarity Functions-based Domain Adaptation with Stacking., , , , , , , , , и . TRECVID, National Institute of Standards and Technology (NIST), (2011)Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting., , , , , , и . ECML/PKDD (3), том 12459 из Lecture Notes in Computer Science, стр. 141-157. Springer, (2020)Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound., , , , , , и . NeurIPS, стр. 455-467. (2021)A PAC-Bayes Analysis of Adversarial Robustness., , , и . NeurIPS, стр. 14421-14433. (2021)A general framework for the practical disintegration of PAC-Bayesian bounds., , , и . Mach. Learn., 113 (2): 519-604 (февраля 2024)Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior., , и . AISTATS, том 89 из Proceedings of Machine Learning Research, стр. 768-776. PMLR, (2019)Metric Learning from Imbalanced Data., , , и . ICTAI, стр. 923-930. IEEE, (2019)Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound., , , и . ECML/PKDD (2), том 12976 из Lecture Notes in Computer Science, стр. 167-183. Springer, (2021)Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures., , , , и . AISTATS, том 238 из Proceedings of Machine Learning Research, стр. 3007-3015. PMLR, (2024)A Theoretically Grounded Extension of Universal Attacks from the Attacker's Viewpoint., , , , , и . ECML/PKDD (4), том 14944 из Lecture Notes in Computer Science, стр. 283-300. Springer, (2024)