Lie He

According to our database, Lie He authored at least 13 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

CoBo: Collaborative Learning via Bilevel Optimization.

Diba Hashemi, Lie He, Martin Jaggi

CoRR, 2024

2023

Provably Personalized and Robust Federated Learning.

Mariel A. Werner, Lie He, Michael I. Jordan, Martin Jaggi, Sai Praneeth Karimireddy

Trans. Mach. Learn. Res., 2023

Debiasing Conditional Stochastic Optimization.

Lie He, Shiva Prasad Kasiviswanathan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Byzantine-Robust Decentralized Learning via Self-Centered Clipping.

Lie He, Sai Praneeth Karimireddy, Martin Jaggi

CoRR, 2022

Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing.

Sai Praneeth Karimireddy, Lie He, Martin Jaggi

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Advances and Open Problems in Federated Learning.

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

Found. Trends Mach. Learn., 2021

RelaySum for Decentralized Deep Learning on Heterogeneous Data.

Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning from History for Byzantine Robust Optimization.

Sai Praneeth Karimireddy, Lie He, Martin Jaggi

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Byzantine-Robust Learning on Heterogeneous Datasets via Resampling.

Lie He, Sai Praneeth Karimireddy, Martin Jaggi

CoRR, 2020

Secure Byzantine-Robust Machine Learning.

Lie He, Sai Praneeth Karimireddy, Martin Jaggi

CoRR, 2020

2019

Advances and Open Problems in Federated Learning.

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

CoRR, 2019

2018

COLA: Communication-Efficient Decentralized Linear Learning.

Lie He, An Bian, Martin Jaggi

CoRR, 2018

COLA: Decentralized Linear Learning.

Lie He, An Bian, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018