Communication-Efficient Distributed Primal-Dual Algorithm for Saddle Point Problem. - Yaodong Yu; Sulin Liu; Sinno Jialin Pan (UAI, 2017)

Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently. - Yaodong Yu; Difan Zou; Quanquan Gu (CoRR, 2017)

Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima. - Yaodong Yu; Pan Xu 002; Quanquan Gu (CoRR, 2017)

Data Poisoning Attacks on Multi-Task Relationship Learning. - Mengchen Zhao; Bo An 001; Yaodong Yu; Sulin Liu; Sinno Jialin Pan (AAAI, 2018)

A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery. - Xiao Zhang 016; Lingxiao Wang 001; Yaodong Yu; Quanquan Gu (ICML, 2018)

Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima. - Yaodong Yu; Pan Xu 002; Quanquan Gu (NeurIPS, 2018)

Learning One-hidden-layer ReLU Networks via Gradient Descent. - Xiao Zhang 016; Yaodong Yu; Lingxiao Wang 001; Quanquan Gu (AISTATS, 2019)

Theoretically Principled Trade-off between Robustness and Accuracy. - Hongyang Zhang 001; Yaodong Yu; Jiantao Jiao; Eric P. Xing; Laurent El Ghaoui; Michael I. Jordan (ICML, 2019)

Rethinking Bias-Variance Trade-off for Generalization of Neural Networks. - Zitong Yang; Yaodong Yu; Chong You; Jacob Steinhardt; Yi Ma 001 (ICML, 2020)

Boundary thickness and robustness in learning models. - Yaoqing Yang; Rajiv Khanna; Yaodong Yu; Amir Gholami; Kurt Keutzer; Joseph E. Gonzalez; Kannan Ramchandran; Michael W. Mahoney (NeurIPS, 2020)

Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction. - Yaodong Yu; Kwan Ho Ryan Chan; Chong You; Chaobing Song; Yi Ma 001 (NeurIPS, 2020)

Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients. - Yifei Huang; Yaodong Yu; Hongyang Zhang 001; Yi Ma 001; Yuan Yao (CoRR, 2020)

Deep Networks from the Principle of Rate Reduction. - Kwan Ho Ryan Chan; Yaodong Yu; Chong You; Haozhi Qi; John Wright 001; Yi Ma 001 (CoRR, 2020)

Adversarial Robustness of Stabilized Neural ODE Might be from Obfuscated Gradients. - Yifei Huang; Yaodong Yu; Hongyang Zhang 001; Yi Ma 001; Yuan Yao (MSML, 2021)

Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition. - Yaodong Yu; Zitong Yang; Edgar Dobriban; Jacob Steinhardt; Yi Ma 001 (CoRR, 2021)

On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging. - Chris Junchi Li; Yaodong Yu; Nicolas Loizou; Gauthier Gidel; Yi Ma 001; Nicolas Le Roux; Michael I. Jordan (CoRR, 2021)

Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction. - Xili Dai; Shengbang Tong; Mingyang Li; Ziyang Wu; Kwan Ho Ryan Chan; Pengyuan Zhai; Yaodong Yu; Michael Psenka; Xiaojun Yuan 002; Heung-Yeung Shum; Yi Ma 001 (CoRR, 2021)

The Effect of Model Size on Worst-Group Generalization. - Alan Pham; Eunice Chan; Vikranth Srivatsa; Dhruba Ghosh; Yaoqing Yang; Yaodong Yu; Ruiqi Zhong; Joseph E. Gonzalez; Jacob Steinhardt (CoRR, 2021)

On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. - Chris Junchi Li; Yaodong Yu; Nicolas Loizou; Gauthier Gidel; Yi Ma 001; Nicolas Le Roux; Michael I. Jordan (AISTATS, 2022)

Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. - Yaodong Yu; Tianyi Lin; Eric V. Mazumdar; Michael I. Jordan (AISTATS, 2022)

Conditional Supervised Contrastive Learning for Fair Text Classification. - Jianfeng Chi; William Shand; Yaodong Yu; Kai-Wei Chang; Han Zhao 002; Yuan Tian 001 (EMNLP (Findings), 2022)

Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. - Tianyi Lin; Aldo Pacchiano; Yaodong Yu; Michael I. Jordan (ICML, 2022)

Predicting Out-of-Distribution Error with the Projection Norm. - Yaodong Yu; Zitong Yang; Alexander Wei 001; Yi Ma 001; Jacob Steinhardt (ICML, 2022)

What You See is What You Get: Principled Deep Learning via Distributional Generalization. - Bogdan Kulynych; Yao-Yuan Yang; Yaodong Yu; Jaroslaw Blasiok; Preetum Nakkiran (NeurIPS, 2022)

TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. - Yaodong Yu; Alexander Wei 001; Sai Praneeth Karimireddy; Yi Ma 001; Michael I. Jordan (NeurIPS, 2022)

Robust Calibration with Multi-domain Temperature Scaling. - Yaodong Yu; Stephen Bates; Yi Ma 001; Michael I. Jordan (NeurIPS, 2022)

What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning. - Bogdan Kulynych; Yao-Yuan Yang; Yaodong Yu; Jaroslaw Blasiok; Preetum Nakkiran (CoRR, 2022)

CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction. - Xili Dai; Shengbang Tong; Mingyang Li; Ziyang Wu; Michael Psenka; Kwan Ho Ryan Chan; Pengyuan Zhai; Yaodong Yu; Xiaojun Yuan 002; Heung-Yeung Shum; Yi Ma 001 (Entropy, 2022)

ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction. - Kwan Ho Ryan Chan; Yaodong Yu; Chong You; Haozhi Qi; John Wright 001; Yi Ma 001 (J. Mach. Learn. Res., 2022)

Federated Conformal Predictors for Distributed Uncertainty Quantification. - Charles Lu 001; Yaodong Yu; Sai Praneeth Karimireddy; Michael I. Jordan; Ramesh Raskar (ICML, 2023)

White-Box Transformers via Sparse Rate Reduction. - Yaodong Yu; Sam Buchanan; Druv Pai; Tianzhe Chu; Ziyang Wu; Shengbang Tong; Benjamin D. Haeffele; Yi Ma 001 (NeurIPS, 2023)

Scaff-PD: Communication Efficient Fair and Robust Federated Learning. - Yaodong Yu; Sai Praneeth Karimireddy; Yi Ma 001; Michael I. Jordan (CoRR, 2023)

White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is? - Yaodong Yu; Sam Buchanan; Druv Pai; Tianzhe Chu; Ziyang Wu; Shengbang Tong; Hao Bai; Yuexiang Zhai; Benjamin D. Haeffele; Yi Ma 001 (CoRR, 2023)

Emergence of Segmentation with Minimalistic White-Box Transformers. - Yaodong Yu; Tianzhe Chu; Shengbang Tong; Ziyang Wu; Druv Pai; Sam Buchanan; Yi Ma 001 (CPAL, 2024)

Masked Completion via Structured Diffusion with White-Box Transformers. - Druv Pai; Sam Buchanan; Ziyang Wu; Yaodong Yu; Yi Ma 001 (ICLR, 2024)

A Global Geometric Analysis of Maximal Coding Rate Reduction. - Peng Wang 098; Huikang Liu; Druv Pai; Yaodong Yu; Zhihui Zhu; Qing Qu 001; Yi Ma 001 (ICML, 2024)

Differentially Private Representation Learning via Image Captioning. - Tom Sander; Yaodong Yu; Maziar Sanjabi; Alain Oliviero Durmus; Yi Ma 001; Kamalika Chaudhuri; Chuan Guo 001 (ICML, 2024)

ViP: A Differentially Private Foundation Model for Computer Vision. - Yaodong Yu; Maziar Sanjabi; Yi Ma 001; Kamalika Chaudhuri; Chuan Guo 001 (ICML, 2024)

A Study on the Calibration of In-context Learning. - Hanlin Zhang 002; Yifan Zhang 004; Yaodong Yu; Dhruv Madeka; Dean P. Foster; Eric P. Xing; Himabindu Lakkaraju; Sham M. Kakade (NAACL-HLT, 2024)

Scaling White-Box Transformers for Vision. - Jinrui Yang; Xianhang Li; Druv Pai; Yuyin Zhou; Yi Ma 001; Yaodong Yu; Cihang Xie (NeurIPS, 2024)

Causal Image Modeling for Efficient Visual Understanding. - Feng Wang 047; Timing Yang; Yaodong Yu; Sucheng Ren; Guoyizhe Wei; Angtian Wang; Wei Shao 008; Yuyin Zhou; Alan L. Yuille; Cihang Xie (CoRR, 2024)

M-VAR: Decoupled Scale-wise Autoregressive Modeling for High-Quality Image Generation. - Sucheng Ren; Yaodong Yu; Nataniel Ruiz; Feng Wang 047; Alan L. Yuille; Cihang Xie (CoRR, 2024)

Reliable Representation Learning: Theory and Practice - Yaodong Yu (2024)

Scheduling a multi-agent flow shop with two scenarios and release dates. - Xinyue Wang; Tao Ren 002; Danyu Bai; Feng Chu 001; Yaodong Yu; Fanchun Meng; Chin-Chia Wu (Int. J. Prod. Res., 2024)

Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation. - Amartya Sanyal; Yaxi Hu; Yaodong Yu; Yian Ma; Yixin Wang; Bernhard Schölkopf (AISTATS, 2025)

Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency. - Feng Wang 047; Timing Yang; Yaodong Yu; Sucheng Ren; Guoyizhe Wei; Angtian Wang; Wei Shao 008; Yuyin Zhou; Alan L. Yuille; Cihang Xie (CVPR, 2025)

Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction. - Ziyang Wu; Tianjiao Ding; Yifu Lu; Druv Pai; Jingyuan Zhang; Weida Wang; Yaodong Yu; Yi Ma 001; Benjamin David Haeffele (ICLR, 2025)

Large-scale information retrieval and correction of noisy pharmacogenomic datasets through residual thresholded deep matrix factorization. - Zhiyue Tom Hu; Yaodong Yu; Ruoqiao Chen; Shan-Ju Yeh; Bin Chen; Haiyan Huang (Briefings Bioinform., 2025)

Trading Inference-Time Compute for Adversarial Robustness. - Wojciech Zaremba; Evgenia Nitishinskaya; Boaz Barak; Stephanie Lin; Sam Toyer; Yaodong Yu; Rachel Dias; Eric Wallace; Kai Xiao; Johannes Heidecke; Amelia Glaese (CoRR, 2025)

Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More. - Feng Wang 047; Yaodong Yu; Guoyizhe Wei; Wei Shao 008; Yuyin Zhou; Alan L. Yuille; Cihang Xie (CoRR, 2025)

Attention-Only Transformers via Unrolled Subspace Denoising. - Peng Wang 098; Yifu Lu; Yaodong Yu; Druv Pai; Qing Qu 001; Yi Ma 001 (CoRR, 2025)