Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Zhouyuan Huo"'
Publikováno v:
Machine Learning.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::98d3ac6c39cdb7fca641b17a0ab39833
https://doi.org/10.1007/s10994-023-06339-4
https://doi.org/10.1007/s10994-023-06339-4
Autor:
Zhouyuan Huo, Dongseong Hwang, Khe Chai Sim, Shefali Garg, Ananya Misra, Nikhil Siddhartha, Trevor Strohman, Francoise Beaufays
Publikováno v:
Interspeech 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd44d65b7052c2ea3c0787b3fa218bdc
https://doi.org/10.21437/interspeech.2022-10904
https://doi.org/10.21437/interspeech.2022-10904
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:7883-7890
Training deep neural networks using a large batch size has shown promising results and benefits many real-world applications. Warmup is one of nontrivial techniques to stabilize the convergence of large batch training. However, warmup is an empirical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41d2fbbbd22dd2595eb8e96a616b1e37
https://doi.org/10.1609/aaai.v35i9.16962
https://doi.org/10.1609/aaai.v35i9.16962
Autor:
Shefali Garg, Khe Chai Sim, Dongseong Hwang, Arun Narayanan, Nikhil Siddhartha, Ananya Misra, Zhouyuan Huo
Publikováno v:
Interspeech 2021.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e66c8c666d350870e18f490fbe55f7d6
https://doi.org/10.21437/interspeech.2021-654
https://doi.org/10.21437/interspeech.2021-654
Publikováno v:
AAAI
Proximal gradient method has been playing an important role to solve many machine learning tasks, especially for the nonsmooth problems. However, in some machine learning problems such as the bandit model and the black-box learning problem, proximal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86dc17ed196c0a5ec9ecff34f37d47f0
https://doi.org/10.1609/aaai.v33i01.33011503
https://doi.org/10.1609/aaai.v33i01.33011503
Publikováno v:
AAAI
Pairwise learning is an important learning topic in the machine learning community, where the loss function involves pairs of samples (e.g., AUC maximization and metric learning). Existing pairwise learning algorithms do not perform well in the gener
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1ed9c643a380922b04705c528a29bc1d
https://doi.org/10.1609/aaai.v33i01.33013697
https://doi.org/10.1609/aaai.v33i01.33013697
Publikováno v:
IEEE transactions on neural networks and learning systems. 33(11)
The privacy-preserving federated learning for vertically partitioned (VP) data has shown promising results as the solution of the emerging multiparty joint modeling application, in which the data holders (such as government branches, private finance,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8700c8f43241c528bfb3cf116a5abe23
https://pubmed.ncbi.nlm.nih.gov/34161243
https://pubmed.ncbi.nlm.nih.gov/34161243
Publikováno v:
IEEE transactions on pattern analysis and machine intelligence. 44(7)
Kernel methods have achieved tremendous success in the past two decades. In the current big data era, data collection has grown tremendously. However, existing kernel methods are not scalable enough both at the training and predicting steps. To addre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::20362b48cbbdd257f7c58da5c0373eb2
https://pubmed.ncbi.nlm.nih.gov/33591910
https://pubmed.ncbi.nlm.nih.gov/33591910
Autor:
Dongseong Hwang, Ananya Misra, Zhouyuan Huo, Nikhil Siddhartha, Shefali Garg, David Qiu, Khe Chai Sim, Trevor Strohman, Francoise Beaufays, Yanzhang He
Self- and semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance the model performance. However, the approach mostly focus on in-domain performance for public datasets. In this study, we utilize th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b718b7312c309fb9365fb75efcab212
Autor:
Lie He, Sebastian U. Stich, Mariana Raykova, Phillip B. Gibbons, Mehryar Mohri, David Evans, Badih Ghazi, Felix X. Yu, Sen Zhao, Jianyu Wang, Zheng Xu, Weikang Song, Prateek Mittal, Ramesh Raskar, Zachary Garrett, Farinaz Koushanfar, H. Brendan McMahan, Ayfer Ozgur, Mikhail Khodak, Rafael G. L. D'Oliveira, Jakub Konecní, Aurélien Bellet, Arjun Nitin Bhagoji, Hubert Eichner, Han Yu, Adrià Gascón, Ananda Theertha Suresh, Sanmi Koyejo, Praneeth Vepakomma, Josh Gardner, Chaoyang He, Florian Tramèr, Tancrède Lepoint, Salim El Rouayheb, Peter Kairouz, Li Xiong, Kallista Bonawitz, Rasmus Pagh, Tara Javidi, Mehdi Bennis, Dawn Song, Martin Jaggi, Zhouyuan Huo, Hang Qi, Gauri Joshi, Qiang Yang, Richard Nock, Yang Liu, Brendan Avent, Justin Hsu, Rachel Cummings, Graham Cormode, Marco Gruteser, Aleksandra Korolova, Ziteng Sun, Zaid Harchaoui, Ben Hutchinson, Zachary Charles, Daniel Ramage
Publikováno v:
Foundations and Trends in Machine Learning
Foundations and Trends in Machine Learning, 2021, 14 (1-2), pp.1-210
Foundations and Trends in Machine Learning, Now Publishers, 2021, 14 (1-2), pp.1-210
Foundations and Trends in Machine Learning, 2021, 14 (1-2), pp.1-210
Foundations and Trends in Machine Learning, Now Publishers, 2021, 14 (1-2), pp.1-210
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b1ccc10027ba1ce68ce0210510e8bdc
https://inria.hal.science/hal-02406503v2/document
https://inria.hal.science/hal-02406503v2/document