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Hongzhou Lin
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2020 – today
- 2024
- [i7]Yijiang River Dong, Hongzhou Lin, Mikhail Belkin, Ramón Huerta, Ivan Vulic:
Unmemorization in Large Language Models via Self-Distillation and Deliberate Imagination. CoRR abs/2402.10052 (2024) - 2023
- [i6]Qingyi Wang, Shenhao Wang, Yunhan Zheng, Hongzhou Lin, Xiaohu Zhang, Jinhua Zhao, Joan Walker:
Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis? CoRR abs/2303.04204 (2023) - 2022
- [c7]Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie:
Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity. ICML 2022: 26347-26361 - 2021
- [c6]Ligeng Zhu, Hongzhou Lin, Yao Lu, Yujun Lin, Song Han:
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning. NeurIPS 2021: 29995-30007 - 2020
- [c5]Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie:
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. ICML 2020: 11173-11182 - [c4]Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gürbüzbalaban, Stefanie Jegelka, Hongzhou Lin:
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. NeurIPS 2020 - [i5]Yossi Arjevani, Amit Daniely, Stefanie Jegelka, Hongzhou Lin:
On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions. CoRR abs/2002.03273 (2020) - [i4]Jingzhao Zhang, Hongzhou Lin, Suvrit Sra, Ali Jadbabaie:
On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions. CoRR abs/2002.04130 (2020) - [i3]Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie:
Stochastic Optimization with Non-stationary Noise. CoRR abs/2006.04429 (2020) - [i2]Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gürbüzbalaban, Stefanie Jegelka, Hongzhou Lin:
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. CoRR abs/2006.06733 (2020)
2010 – 2019
- 2019
- [j2]Hongzhou Lin, Julien Mairal, Zaïd Harchaoui:
An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration. SIAM J. Optim. 29(2): 1408-1443 (2019) - 2018
- [c3]Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaïd Harchaoui:
Catalyst for Gradient-based Nonconvex Optimization. AISTATS 2018: 613-622 - [c2]Hongzhou Lin, Stefanie Jegelka:
ResNet with one-neuron hidden layers is a Universal Approximator. NeurIPS 2018: 6172-6181 - [i1]Hongzhou Lin, Stefanie Jegelka:
ResNet with one-neuron hidden layers is a Universal Approximator. CoRR abs/1806.10909 (2018) - 2017
- [b1]Hongzhou Lin:
Generic acceleration schemes for gradient-based optimization in machine learning. (Algorithmes d'accélération générique pour les méthodes d'optimisation en apprentissage statistique). Grenoble Alpes University, France, 2017 - [j1]Hongzhou Lin, Julien Mairal, Zaïd Harchaoui:
Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice. J. Mach. Learn. Res. 18: 212:1-212:54 (2017) - 2015
- [c1]Hongzhou Lin, Julien Mairal, Zaïd Harchaoui:
A Universal Catalyst for First-Order Optimization. NIPS 2015: 3384-3392
Coauthor Index
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