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Shiqi He
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2020 – today
- 2024
- [j1]Hongwei Chen, Shiqi He, Zexi Chen:
DSTC-Net: differential spatio-temporal correlation network for similar action recognition. Multim. Syst. 30(3): 153 (2024) - [c6]Zhifeng Jiang, Peng Ye, Shiqi He, Wei Wang, Ruichuan Chen, Bo Li:
Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning. USENIX Security Symposium 2024 - [i6]Zhifeng Jiang, Peng Ye, Shiqi He, Wei Wang, Ruichuan Chen, Bo Li:
Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning. CoRR abs/2401.02880 (2024) - 2023
- [c5]Feijie Wu, Song Guo, Zhihao Qu, Shiqi He, Ziming Liu, Jing Gao:
Anchor Sampling for Federated Learning with Partial Client Participation. ICML 2023: 37379-37416 - [c4]Shiqi He, Qifan Yan, Feijie Wu, Lanjun Wang, Mathias Lécuyer, Ivan Beschastnikh:
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning. MLSys 2023 - [i5]Rindranirina Ramamonjison, Timothy T. L. Yu, Raymond Li, Haley Li, Giuseppe Carenini, Bissan Ghaddar
, Shiqi He, Mahdi Mostajabdaveh, Amin Banitalebi-Dehkordi, Zirui Zhou, Yong Zhang:
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions. CoRR abs/2303.08233 (2023) - 2022
- [c3]Feijie Wu, Shiqi He, Song Guo, Zhihao Qu, Haozhao Wang, Weihua Zhuang, Jie Zhang:
Sign bit is enough: a learning synchronization framework for multi-hop all-reduce with ultimate compression. DAC 2022: 193-198 - [c2]Rindra Ramamonjison, Haley Li, Timothy T. L. Yu, Shiqi He, Vishnu Rengan, Amin Banitalebi-Dehkordi, Zirui Zhou, Yong Zhang:
Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions. EMNLP (Industry Track) 2022: 29-62 - [i4]Feijie Wu, Shiqi He, Song Guo, Zhihao Qu, Haozhao Wang, Weihua Zhuang, Jie Zhang:
Sign Bit is Enough: A Learning Synchronization Framework for Multi-hop All-reduce with Ultimate Compression. CoRR abs/2204.06787 (2022) - [i3]Feijie Wu, Song Guo, Zhihao Qu, Shiqi He, Ziming Liu:
Accelerating Federated Learning via Sampling Anchor Clients with Large Batches. CoRR abs/2206.05891 (2022) - [i2]Rindranirina Ramamonjison, Haley Li, Timothy T. L. Yu, Shiqi He, Vishnu Rengan, Amin Banitalebi-Dehkordi, Zirui Zhou, Yong Zhang:
Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions. CoRR abs/2209.15565 (2022) - [i1]Shiqi He, Qifan Yan, Feijie Wu, Lanjun Wang, Mathias Lécuyer, Ivan Beschastnikh:
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning. CoRR abs/2212.01523 (2022) - 2021
- [c1]Rindranirina Ramamonjison, Timothy T. L. Yu, Raymond Li, Haley Li, Giuseppe Carenini, Bissan Ghaddar, Shiqi He, Mahdi Mostajabdaveh, Amin Banitalebi-Dehkordi, Zirui Zhou, Yong Zhang:
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions. NeurIPS (Competition and Demos) 2021: 189-203
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