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2020 – today
- 2024
[i10]Diba Hashemi, Lie He, Martin Jaggi:
CoBo: Collaborative Learning via Bilevel Optimization. CoRR abs/2409.05539 (2024)- 2023
- [j2]Mariel A. Werner, Lie He, Michael I. Jordan, Martin Jaggi, Sai Praneeth Karimireddy:
Provably Personalized and Robust Federated Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c5]Lie He, Shiva Prasad Kasiviswanathan:
Debiasing Conditional Stochastic Optimization. NeurIPS 2023 - [i9]Lie He, Shiva Prasad Kasiviswanathan:
Debiasing Conditional Stochastic Optimization. CoRR abs/2304.10613 (2023) - [i8]Mariel A. Werner, Lie He, Sai Praneeth Karimireddy, Michael I. Jordan, Martin Jaggi:
Provably Personalized and Robust Federated Learning. CoRR abs/2306.08393 (2023) - 2022
- [c4]Sai Praneeth Karimireddy, Lie He, Martin Jaggi:
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing. ICLR 2022 - [i7]Lie He, Sai Praneeth Karimireddy, Martin Jaggi:
Byzantine-Robust Decentralized Learning via Self-Centered Clipping. CoRR abs/2202.01545 (2022) - 2021
- [j1]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis
, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - [c3]Sai Praneeth Karimireddy, Lie He, Martin Jaggi:
Learning from History for Byzantine Robust Optimization. ICML 2021: 5311-5319 - [c2]Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi:
RelaySum for Decentralized Deep Learning on Heterogeneous Data. NeurIPS 2021: 28004-28015 - [i6]Thijs Vogels, Lie He, Anastasia Koloskova, Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
RelaySum for Decentralized Deep Learning on Heterogeneous Data. CoRR abs/2110.04175 (2021) - 2020
- [i5]Lie He, Sai Praneeth Karimireddy, Martin Jaggi:
Secure Byzantine-Robust Machine Learning. CoRR abs/2006.04747 (2020) - [i4]Lie He, Sai Praneeth Karimireddy, Martin Jaggi:
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling. CoRR abs/2006.09365 (2020) - [i3]Sai Praneeth Karimireddy, Lie He, Martin Jaggi:
Learning from History for Byzantine Robust Optimization. CoRR abs/2012.10333 (2020)
2010 – 2019
- 2019
- [i2]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - 2018
- [c1]Lie He, An Bian, Martin Jaggi:
COLA: Decentralized Linear Learning. NeurIPS 2018: 4541-4551 - [i1]Lie He, An Bian, Martin Jaggi:
COLA: Communication-Efficient Decentralized Linear Learning. CoRR abs/1808.04883 (2018)
Coauthor Index
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