Lie He

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 16

Top Topics

Heterogeneous Data

Machine Learning

Inductive Inference

Top Venues

Publications

Mariel A. Werner, Lie He, Sai Praneeth Karimireddy, Michael I. Jordan, Martin Jaggi
Provably Personalized and Robust Federated Learning. CoRR (2023)
Lie He, Shiva Prasad Kasiviswanathan
Debiasing Conditional Stochastic Optimization. NeurIPS (2023)
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)
Lie He, Shiva Prasad Kasiviswanathan
Debiasing Conditional Stochastic Optimization. CoRR (2023)
Sai Praneeth Karimireddy, Lie He, Martin Jaggi
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing. ICLR (2022)
Lie He, Sai Praneeth Karimireddy, Martin Jaggi
Byzantine-Robust Decentralized Learning via Self-Centered Clipping. CoRR (2022)
Sai Praneeth Karimireddy, Lie He, Martin Jaggi
Learning from History for Byzantine Robust Optimization. ICML (2021)
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 (2021)
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)
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) (2021)
Lie He, Sai Praneeth Karimireddy, Martin Jaggi
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling. CoRR (2020)
Sai Praneeth Karimireddy, Lie He, Martin Jaggi
Learning from History for Byzantine Robust Optimization. CoRR (2020)
Lie He, Sai Praneeth Karimireddy, Martin Jaggi
Secure Byzantine-Robust Machine Learning. CoRR (2020)
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 (2019)
Lie He, An Bian, Martin Jaggi
COLA: Communication-Efficient Decentralized Linear Learning. CoRR (2018)
Lie He, An Bian, Martin Jaggi
COLA: Decentralized Linear Learning. NeurIPS (2018)