Yin, Dong

18 publications

ICLR 2025 Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlo Shengyu Feng, Xiang Kong, Shuang Ma, Aonan Zhang, Dong Yin, Chong Wang, Ruoming Pang, Yiming Yang

NeurIPSW 2024 Memory Retaining Finetuning via Distillation Zitong Yang, Aonan Zhang, Sam Wiseman, Xiang Kong, Ke Ye, Dong Yin

NeurIPSW 2024 Model Soup for Better RLHF: Weight Space Averaging to Improve Alignment in LLMs Atoosa Chegini, Hamid Kazemi, Seyed Iman Mirzadeh, Dong Yin, Maxwell Horton, Moin Nabi, Mehrdad Farajtabar, Keivan Alizadeh

MLJ 2023 An Instance-Dependent Simulation Framework for Learning with Label Noise Keren Gu, Xander Masotto, Vandana Bachani, Balaji Lakshminarayanan, Jack Nikodem, Dong Yin

AISTATS 2022 Confident Least Square Value Iteration with Local Access to a Simulator Botao Hao, Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvari

ALT 2022 Efficient Local Planning with Linear Function Approximation Dong Yin, Botao Hao, Yasin Abbasi-Yadkori, Nevena Lazić, Csaba Szepesvári

ICML 2022 Wide Neural Networks Forget Less Catastrophically Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin, Huiyi Hu, Razvan Pascanu, Dilan Gorur, Mehrdad Farajtabar

ICML 2021 Improved Regret Bound and Experience Replay in Regularized Policy Iteration Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvari

NeurIPS 2020 A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs Nevena Lazic, Dong Yin, Mehrdad Farajtabar, Nir Levine, Dilan Gorur, Chris Harris, Dale Schuurmans

NeurIPS 2020 An Efficient Framework for Clustered Federated Learning Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran

ICML 2020 Stochastic Gradient and Langevin Processes Xiang Cheng, Dong Yin, Peter Bartlett, Michael Jordan

ECCVW 2020 VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results Dawei Du, Longyin Wen, Pengfei Zhu, Heng Fan, Qinghua Hu, Haibin Ling, Mubarak Shah, Junwen Pan, Apostolos Axenopoulos, Arne Schumann, Athanasios Psaltis, Ayush Jain, Bin Dong, Changlin Li, Chen Chen, Chengzhen Duan, Chongyang Zhang, Daniel Stadler, Dheeraj Reddy Pailla, Dong Yin, Faizan Khan, Fanman Meng, Guangyu Gao, Guosheng Zhang, Hansheng Chen, Hao Zhou, Haonian Xie, Heqian Qiu, Hongliang Li, Ioannis Athanasiadis, Jincai Cui, Jingkai Zhou, Jong Hwan Ko, Joo Chan Lee, Jun Yu, Jungyeop Yoo, Lars Wilko Sommer, Lu Xiong, Michael Schleiss, Ming-Hsuan Yang, Mingyu Liu, Minjian Zhang, Murari Mandal, Petros Daras, Pratik Narang, Qiong Liu, Qiu Shi, Qizhang Lin, Rohit Ramaprasad, Sai Wang, Sarvesh Mehta, Shuai Li, Shuqin Huang, Sungtae Moon, Taijin Zhao, Ting Sun, Wei Guo, Wei Tian, Weida Qin, Weiping Yu, Wenxiang Lin, Xi Zhao, Xiaogang Jia, Xin He, Xingjie Zhao, Xuanxin Liu, Yan Ding, Yan Luo, Yang Xiao, Yi Wang, Yingjie Liu, Yongwoo Kim, Yu Sun, Yuehan Yao, Yuyao Huang, Zehui Gong, Zhenyu Xu, Zhipeng Luo, Zhiguo Cao, Zhiwei Wei, Zhongjie Fan, Zichen Song, Ziming Liu

NeurIPS 2019 A Fourier Perspective on Model Robustness in Computer Vision Dong Yin, Raphael Gontijo Lopes, Jon Shlens, Ekin Dogus Cubuk, Justin Gilmer

ICML 2019 Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning Dong Yin, Yudong Chen, Ramchandran Kannan, Peter Bartlett

ICML 2019 Rademacher Complexity for Adversarially Robust Generalization Dong Yin, Ramchandran Kannan, Peter Bartlett

ICML 2018 Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates Dong Yin, Yudong Chen, Ramchandran Kannan, Peter Bartlett

AISTATS 2018 Gradient Diversity: A Key Ingredient for Scalable Distributed Learning Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett

JMLR 2017 Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks Adam S. Charles, Dong Yin, Christopher J. Rozell