Lie He - RankMe

21 papers A* 7Journal 14

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective. ICML Yunzhen Yao, Lie He, Michael Gastpar
2025	J	`jnl`	Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective. CoRR Yunzhen Yao, Lie He, Michael Gastpar
2025	J	`jnl`	Non-Asymptotic Analysis of Efficiency in Conformalized Regression. CoRR Yunzhen Yao, Lie He, Michael Gastpar
2024	A*	`conf`	CoBo: Collaborative Learning via Bilevel Optimization. NeurIPS Diba Hashemi, Lie He, Martin Jaggi
2024	J	`jnl`	CoBo: Collaborative Learning via Bilevel Optimization. CoRR Diba Hashemi, Lie He, Martin Jaggi
2023	A*	`conf`	Debiasing Conditional Stochastic Optimization. NeurIPS Lie He, Shiva Prasad Kasiviswanathan
2023	J	`jnl`	Debiasing Conditional Stochastic Optimization. CoRR Lie He, Shiva Prasad Kasiviswanathan
2023	J	`jnl`	Provably Personalized and Robust Federated Learning. CoRR Mariel A. Werner, Lie He, Sai Praneeth Karimireddy, Michael I. Jordan, Martin Jaggi
2023	J	`jnl`	Provably Personalized and Robust Federated Learning. Trans. Mach. Learn. Res. Mariel A. Werner, Lie He, Michael I. Jordan, Martin Jaggi, Sai Praneeth Karimireddy
2022	J	`jnl`	Byzantine-Robust Decentralized Learning via Self-Centered Clipping. CoRR Lie He, Sai Praneeth Karimireddy, Martin Jaggi
2022	A*	`conf`	Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing. ICLR Sai Praneeth Karimireddy, Lie He, Martin Jaggi
2021	J	`jnl`	Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 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
2021	A*	`conf`	Learning from History for Byzantine Robust Optimization. ICML Sai Praneeth Karimireddy, Lie He, Martin Jaggi
2021	A*	`conf`	RelaySum for Decentralized Deep Learning on Heterogeneous Data. NeurIPS Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi
2021	J	`jnl`	RelaySum for Decentralized Deep Learning on Heterogeneous Data. CoRR Thijs Vogels, Lie He, Anastasia Koloskova, Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
2020	J	`jnl`	Byzantine-Robust Learning on Heterogeneous Datasets via Resampling. CoRR Lie He, Sai Praneeth Karimireddy, Martin Jaggi
2020	J	`jnl`	Learning from History for Byzantine Robust Optimization. CoRR Sai Praneeth Karimireddy, Lie He, Martin Jaggi
2020	J	`jnl`	Secure Byzantine-Robust Machine Learning. CoRR Lie He, Sai Praneeth Karimireddy, Martin Jaggi
2019	J	`jnl`	Advances and Open Problems in Federated Learning. CoRR 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
2018	J	`jnl`	COLA: Communication-Efficient Decentralized Linear Learning. CoRR Lie He, An Bian, Martin Jaggi
2018	A*	`conf`	COLA: Decentralized Linear Learning. NeurIPS Lie He, An Bian, Martin Jaggi