A Universal Catalyst for First-Order Optimization. - Hongzhou Lin; Julien Mairal; Zaïd Harchaoui (NIPS, 2015)

Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice. - Hongzhou Lin; Julien Mairal; Zaïd Harchaoui (J. Mach. Learn. Res., 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 (2017)

Catalyst for Gradient-based Nonconvex Optimization. - Courtney Paquette; Hongzhou Lin; Dmitriy Drusvyatskiy; Julien Mairal; Zaïd Harchaoui (AISTATS, 2018)

ResNet with one-neuron hidden layers is a Universal Approximator. - Hongzhou Lin; Stefanie Jegelka (NeurIPS, 2018)

An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration. - Hongzhou Lin; Julien Mairal; Zaïd Harchaoui (SIAM J. Optim., 2019)

Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions. - Jingzhao Zhang; Hongzhou Lin; Stefanie Jegelka; Suvrit Sra; Ali Jadbabaie (ICML, 2020)

IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. - Yossi Arjevani; Joan Bruna; Bugra Can; Mert Gürbüzbalaban; Stefanie Jegelka; Hongzhou Lin (NeurIPS, 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)

On Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions. - Jingzhao Zhang; Hongzhou Lin; Suvrit Sra; Ali Jadbabaie (CoRR, 2020)

Stochastic Optimization with Non-stationary Noise. - Jingzhao Zhang; Hongzhou Lin; Subhro Das; Suvrit Sra; Ali Jadbabaie (CoRR, 2020)

Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning. - Ligeng Zhu; Hongzhou Lin; Yao Lu 006; Yujun Lin 001; Song Han 003 (NeurIPS, 2021)

Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity. - Jingzhao Zhang; Hongzhou Lin; Subhro Das; Suvrit Sra; Ali Jadbabaie (ICML, 2022)

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)

Unmemorization in Large Language Models via Self-Distillation and Deliberate Imagination. - Yijiang River Dong; Hongzhou Lin; Mikhail Belkin; Ramón Huerta; Ivan Vulic (CoRR, 2024)

From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency. - Kaiyue Wen; Huaqing Zhang 005; Hongzhou Lin; Jingzhao Zhang (ICLR, 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 (NAACL (Long Papers), 2025)

Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving. - Yong Lin; Shange Tang; Bohan Lyu 001; Jiayun Wu; Hongzhou Lin; Kaiyu Yang; Jia Li; Mengzhou Xia; Danqi Chen 001; Sanjeev Arora; Chi Jin 001 (CoRR, 2025)

Task Generalization With AutoRegressive Compositional Structure: Can Learning From D Tasks Generalize to DT Tasks? - Amirhesam Abedsoltan; Huaqing Zhang 005; Kaiyue Wen; Hongzhou Lin; Jingzhao Zhang; Mikhail Belkin (CoRR, 2025)

Ineq-Comp: Benchmarking Human-Intuitive Compositional Reasoning in Automated Theorem Proving on Inequalities. - Haoyu Zhao; Yihan Geng; Shange Tang; Yong Lin; Bohan Lyu 001; Hongzhou Lin; Chi Jin 001; Sanjeev Arora (CoRR, 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, 2025)

Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction. - Yong Lin; Shange Tang; Bohan Lyu 001; Ziran Yang; Jui-Hui Chung; Haoyu Zhao; Lai Jiang; Yihan Geng; Jiawei Ge 003; Jingruo Sun; Jiayun Wu; Jiri Gesi; et al. (CoRR, 2025)