Fanny Yang 个人学术档案 - 学术资源搜索

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Konstantin DonhauserETH Zuerich在 ai.ethz.ch 的电子邮件经过验证
Percy LiangAssociate Professor of Computer Science, Stanford University在 cs.stanford.edu 的电子邮件经过验证
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford University在 stanford.edu 的电子邮件经过验证
Sang Michael XiePhD Candidate, Stanford University在 cs.stanford.edu 的电子邮件经过验证
Aditi RaghunathanAssistant professor, Carnegie Mellon University在 cmu.edu 的电子邮件经过验证
Alexandru ȚifreaETH Zurich在 inf.ethz.ch 的电子邮件经过验证
Amartya SanyalMax Planck Institute for Intelligent Systems, Tuebingen在 tuebingen.mpg.de 的电子邮件经过验证
Holger BocheTechnische Universität München在 tum.de 的电子邮件经过验证
Volker PohlTechnische Universität München在 tum.de 的电子邮件经过验证
Aaditya RamdasStatistics and Machine Learning, Carnegie Mellon University在 stat.cmu.edu 的电子邮件经过验证
Anqi (Angie) LiuJohns Hopkins University在 cs.jhu.edu 的电子邮件经过验证
Anima AnandkumarCalifornia Institute of Technology and NVIDIA在 caltech.edu 的电子邮件经过验证
Kamyar AzizzadenesheliNvidia在 nvidia.com 的电子邮件经过验证
Yuting WeiStatistics and Data Science at Wharton, University of Pennsylvania在 wharton.upenn.edu 的电子邮件经过验证
Sivaraman BalakrishnanCarnegie Mellon University在 stat.cmu.edu 的电子邮件经过验证
Kevin JamiesonAssociate Professor, University of Washington在 cs.washington.edu 的电子邮件经过验证
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley在 cs.berkeley.edu 的电子邮件经过验证
Michael AerniDoctoral student at Secure and Private AI (SPY) Lab, ETH Zurich在 inf.ethz.ch 的电子邮件经过验证
Nicolò RuggeriMax Planck Institute for Intelligent Systems - ETH Zurich - Epsilico在 tuebingen.mpg.de 的电子邮件经过验证
Stefan StojanovicKTH在 kth.se 的电子邮件经过验证

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Fanny Yang

ETH Zurich

在 inf.ethz.ch 的电子邮件经过验证 - 首页

Machine Learning Statistical Learning Optimization High-dimensional Statistics


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Adversarial training can hurt generalization A Raghunathan, SM Xie, F Yang, JC Duchi, P Liang arXiv preprint arXiv:1906.06032, 2019	257	2019
Understanding and mitigating the tradeoff between robustness and accuracy A Raghunathan, SM Xie, F Yang, J Duchi, P Liang arXiv preprint arXiv:2002.10716, 2020	223	2020
Regularized learning for domain adaptation under label shifts K Azizzadenesheli, A Liu, F Yang, A Anandkumar arXiv preprint arXiv:1903.09734, 2019	207	2019
Early stopping for kernel boosting algorithms: A general analysis with localized complexities Y Wei, F Yang, MJ Wainwright Advances in Neural Information Processing Systems 30, 2017	87	2017
Online control of the false discovery rate with decaying memory A Ramdas, F Yang, MJ Wainwright, MI Jordan Advances in neural information processing systems 30, 2017	71	2017
A framework for multi-a (rmed)/b (andit) testing with online fdr control F Yang, A Ramdas, KG Jamieson, MJ Wainwright Advances in Neural Information Processing Systems 30, 2017	71	2017
Statistical and computational guarantees for the Baum-Welch algorithm F Yang, S Balakrishnan, MJ Wainwright Journal of Machine Learning Research 18 (125), 1-53, 2017	62	2017
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness F Yang, Z Wang, C Heinze-Deml Advances in Neural Information Processing Systems 32, 2019	41	2019
Phaseless signal recovery in infinite dimensional spaces using structured modulations V Pohl, F Yang, H Boche Journal of Fourier Analysis and Applications 20, 1212-1233, 2014	37	2014
Tight bounds for minimum -norm interpolation of noisy data G Wang, K Donhauser, F Yang International Conference on Artificial Intelligence and Statistics, 10572-10602, 2022	31	2022
How unfair is private learning? A Sanyal, Y Hu, F Yang Uncertainty in Artificial Intelligence, 1738-1748, 2022	22	2022
Fast rates for noisy interpolation require rethinking the effect of inductive bias K Donhauser, N Ruggeri, S Stojanovic, F Yang International Conference on Machine Learning, 5397-5428, 2022	22	2022
How rotational invariance of common kernels prevents generalization in high dimensions K Donhauser, M Wu, F Yang International Conference on Machine Learning, 2804-2814, 2021	22	2021
Phase retrieval via structured modulations in Paley-Wiener spaces F Yang, V Pohl, H Boche arXiv preprint arXiv:1302.4258, 2013	22	2013
Phase retrieval from low-rate samples V Pohl, H Boche, F Yang Sampling Theory in Signal and Image Processing 14, 71-99, 2015	20	2015
Self-supervised reinforcement learning with independently controllable subgoals A Zadaianchuk, G Martius, F Yang Conference on Robot Learning, 384-394, 2022	18	2022
Why adversarial training can hurt robust accuracy J Clarysse, J Hörrmann, F Yang arXiv preprint arXiv:2203.02006, 2022	16	2022
Semi-supervised novelty detection using ensembles with regularized disagreement A Tifrea, E Stavarache, F Yang Uncertainty in Artificial Intelligence, 1939-1948, 2022	14*	2022
Interpolation can hurt robust generalization even when there is no noise K Donhauser, A Tifrea, M Aerni, R Heckel, F Yang Advances in Neural Information Processing Systems 34, 23465-23477, 2021	12	2021
Uniform versus uncertainty sampling: When being active is less efficient than staying passive A Tifrea, J Clarysse, F Yang arXiv preprint arXiv:2212.00772, 2022	9	2022

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