Hongzhou Lin

According to our database, Hongzhou Lin authored at least 22 papers between 2015 and 2025.

Collaborative distances:

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

2025

Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction.

Yong Lin, Shange Tang, Bohan Lyu, Ziran Yang, Jui-Hui Chung, Haoyu Zhao, Lai Jiang, Yihan Geng, Jiawei Ge, Jingruo Sun, Jiayun Wu, Jiri Gesi, Ximing Lu, David Acuna, Kaiyu Yang, Hongzhou Lin, Yejin Choi, Danqi Chen, Sanjeev Arora, Chi Jin

CoRR, August, 2025

QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation.

Jiazheng Li, Hong Lu, Kaiyue Wen, Zaiwen Yang, Jiaxuan Gao, Hongzhou Lin, Yi Wu, Jingzhao Zhang

CoRR, July, 2025

Ineq-Comp: Benchmarking Human-Intuitive Compositional Reasoning in Automated Theorem Proving on Inequalities.

Haoyu Zhao, Yihan Geng, Shange Tang, Yong Lin, Bohan Lyu, Hongzhou Lin, Chi Jin, Sanjeev Arora

CoRR, May, 2025

Task Generalization With AutoRegressive Compositional Structure: Can Learning From <i>D</i> Tasks Generalize to <i>D</i>^T Tasks?

Amirhesam Abedsoltan, Huaqing Zhang, Kaiyue Wen, Hongzhou Lin, Jingzhao Zhang, Mikhail Belkin

CoRR, February, 2025

Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving.

Yong Lin, Shange Tang, Bohan Lyu, Jiayun Wu, Hongzhou Lin, Kaiyu Yang, Jia Li, Mengzhou Xia, Danqi Chen, Sanjeev Arora, Chi Jin

CoRR, February, 2025

UNDIAL: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language Models.

Yijiang River Dong, Hongzhou Lin, Mikhail Belkin, Ramón Huerta, Ivan Vulic

Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, 2025

From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency.

Kaiyue Wen, Huaqing Zhang, Hongzhou Lin, Jingzhao Zhang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Unmemorization in Large Language Models via Self-Distillation and Deliberate Imagination.

Yijiang River Dong, Hongzhou Lin, Mikhail Belkin, Ramón Huerta, Ivan Vulic

CoRR, 2024

2023

Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?

Qingyi Wang, Shenhao Wang, Yunhan Zheng, Hongzhou Lin, Xiaohu Zhang, Jinhua Zhao, Joan Walker

CoRR, 2023

2022

Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity.

Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie

Proceedings of the International Conference on Machine Learning, 2022

2021

Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning.

Ligeng Zhu, Hongzhou Lin, Yao Lu, Yujun Lin, Song Han

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

2020

Stochastic Optimization with Non-stationary Noise.

Jingzhao Zhang, Hongzhou Lin, Subhro Das, Suvrit Sra, Ali Jadbabaie

CoRR, 2020

On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions.

Jingzhao Zhang, Hongzhou Lin, Suvrit Sra, Ali Jadbabaie

CoRR, 2020

On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions.

Yossi Arjevani, Amit Daniely, Stefanie Jegelka, Hongzhou Lin

CoRR, 2020

IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method.

Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gürbüzbalaban, Stefanie Jegelka, Hongzhou Lin

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions.

Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration.

Hongzhou Lin, Julien Mairal, Zaïd Harchaoui

SIAM J. Optim., 2019

2018

ResNet with one-neuron hidden layers is a Universal Approximator.

Hongzhou Lin, Stefanie Jegelka

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

Catalyst for Gradient-based Nonconvex Optimization.

Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaïd Harchaoui

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Generic acceleration schemes for gradient-based optimization in machine learning. (Algorithmes d'accélération générique pour les méthodes d'optimisation en apprentissage statistique).

Hongzhou Lin

PhD thesis, 2017

Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice.

Hongzhou Lin, Julien Mairal, Zaïd Harchaoui

J. Mach. Learn. Res., 2017

2015

A Universal Catalyst for First-Order Optimization.

Hongzhou Lin, Julien Mairal, Zaïd Harchaoui

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015