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Haochuan Li
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2020 – today
- 2024
- [b1]Haochuan Li:
Smoothness and Adaptivity in Nonlinear Optimization for Machine Learning Applications. MIT, USA, 2024 - [i11]Leigang Qu, Haochuan Li, Tan Wang, Wenjie Wang, Yongqi Li, Liqiang Nie, Tat-Seng Chua:
Unified Text-to-Image Generation and Retrieval. CoRR abs/2406.05814 (2024) - 2023
- [c9]Haochuan Li, Jian Qian, Yi Tian, Alexander Rakhlin, Ali Jadbabaie:
Convex and Non-convex Optimization Under Generalized Smoothness. NeurIPS 2023 - [c8]Haochuan Li, Alexander Rakhlin, Ali Jadbabaie:
Convergence of Adam Under Relaxed Assumptions. NeurIPS 2023 - [i10]Amirhossein Reisizadeh, Haochuan Li, Subhro Das, Ali Jadbabaie:
Variance-reduced Clipping for Non-convex Optimization. CoRR abs/2303.00883 (2023) - [i9]Haochuan Li, Ali Jadbabaie, Alexander Rakhlin:
Convergence of Adam Under Relaxed Assumptions. CoRR abs/2304.13972 (2023) - [i8]Haochuan Li, Jian Qian, Yi Tian, Alexander Rakhlin, Ali Jadbabaie:
Convex and Non-Convex Optimization under Generalized Smoothness. CoRR abs/2306.01264 (2023) - 2022
- [c7]Ali Jadbabaie, Haochuan Li, Jian Qian, Yi Tian:
Byzantine-Robust Federated Linear Bandits. CDC 2022: 5206-5213 - [c6]Haochuan Li, Farzan Farnia, Subhro Das, Ali Jadbabaie:
On Convergence of Gradient Descent Ascent: A Tight Local Analysis. ICML 2022: 12717-12740 - [c5]Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie:
Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective. ICML 2022: 26330-26346 - [c4]Pouria Mahdavinia, Yuyang Deng, Haochuan Li, Mehrdad Mahdavi:
Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems. NeurIPS 2022 - [i7]Ali Jadbabaie, Haochuan Li, Jian Qian, Yi Tian:
Byzantine-Robust Federated Linear Bandits. CoRR abs/2204.01155 (2022) - [i6]Haochuan Li, Farzan Farnia, Subhro Das, Ali Jadbabaie:
On Convergence of Gradient Descent Ascent: A Tight Local Analysis. CoRR abs/2207.00957 (2022) - [i5]Pouria Mahdavinia, Yuyang Deng, Haochuan Li, Mehrdad Mahdavi:
Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems. CoRR abs/2210.09382 (2022) - 2021
- [c3]Haochuan Li, Yi Tian, Jingzhao Zhang, Ali Jadbabaie:
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization. NeurIPS 2021: 1792-1804 - [i4]Haochuan Li, Yi Tian, Jingzhao Zhang, Ali Jadbabaie:
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization. CoRR abs/2104.08708 (2021) - [i3]Jingzhao Zhang, Haochuan Li, Suvrit Sra, Ali Jadbabaie:
On Convergence of Training Loss Without Reaching Stationary Points. CoRR abs/2110.06256 (2021)
2010 – 2019
- 2019
- [c2]Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, Xiyu Zhai:
Gradient Descent Finds Global Minima of Deep Neural Networks. ICML 2019: 1675-1685 - [c1]Ruiqi Gao, Tianle Cai, Haochuan Li, Cho-Jui Hsieh, Liwei Wang, Jason D. Lee:
Convergence of Adversarial Training in Overparametrized Neural Networks. NeurIPS 2019: 13009-13020 - [i2]Ruiqi Gao, Tianle Cai, Haochuan Li, Liwei Wang, Cho-Jui Hsieh, Jason D. Lee:
Convergence of Adversarial Training in Overparametrized Networks. CoRR abs/1906.07916 (2019) - 2018
- [i1]Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, Xiyu Zhai:
Gradient Descent Finds Global Minima of Deep Neural Networks. CoRR abs/1811.03804 (2018)
Coauthor Index
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last updated on 2024-08-29 21:51 CEST by the dblp team
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