Yaodong Yu

78 papers A* 23A 5Journal 45Unranked 4
YearRankTypeTitle / Venue / Authors
2026 J jnl
IH-Challenge: A Training Dataset to Improve Instruction Hierarchy on Frontier LLMs.
CoRR
Chuan Guo, Juan Felipe Ceron Uribe, Sicheng Zhu, Christopher A. Choquette-Choo, Stephanie Lin, Nikhil Kandpal, Milad Nasr, Rai, Sam Toyer, Miles Wang, Yaodong Yu, Alex Beutel, Kai Xiao
2025 A conf
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation.
AISTATS
Amartya Sanyal, Yaxi Hu, Yaodong Yu, Yian Ma, Yixin Wang, Bernhard Schölkopf
2025 A* conf
Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency.
CVPR
Feng Wang, Timing Yang, Yaodong Yu, Sucheng Ren, Guoyizhe Wei, Angtian Wang, Wei Shao, Yuyin Zhou, Alan L. Yuille, Cihang Xie
2025 A* conf
Attention-Only Transformers via Unrolled Subspace Denoising.
ICML
Peng Wang, Yifu Lu, Yaodong Yu, Druv Pai, Qing Qu, Yi Ma
2025 J jnl
Attention-Only Transformers via Unrolled Subspace Denoising.
CoRR
Peng Wang, Yifu Lu, Yaodong Yu, Druv Pai, Qing Qu, Yi Ma
2025 J jnl
Large-scale information retrieval and correction of noisy pharmacogenomic datasets through residual thresholded deep matrix factorization.
Briefings Bioinform.
Zhiyue Tom Hu, Yaodong Yu, Ruoqiao Chen, Shan-Ju Yeh, Bin Chen, Haiyan Huang
2025 A* conf
Scaling Laws in Patchification: An Image Is Worth 50, 176 Tokens And More.
ICML
Feng Wang, Yaodong Yu, Wei Shao, Yuyin Zhou, Alan L. Yuille, Cihang Xie
2025 J jnl
Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More.
CoRR
Feng Wang, Yaodong Yu, Guoyizhe Wei, Wei Shao, Yuyin Zhou, Alan L. Yuille, Cihang Xie
2025 A* conf
Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction.
ICLR
Ziyang Wu, Tianjiao Ding, Yifu Lu, Druv Pai, Jingyuan Zhang, Weida Wang, Yaodong Yu, Yi Ma, Benjamin David Haeffele
2025 J jnl
Trading Inference-Time Compute for Adversarial Robustness.
CoRR
Wojciech Zaremba, Evgenia Nitishinskaya, Boaz Barak, Stephanie Lin, Sam Toyer, Yaodong Yu, Rachel Dias, Eric Wallace, Kai Xiao, Johannes Heidecke, Amelia Glaese
2024 A* conf
A Global Geometric Analysis of Maximal Coding Rate Reduction.
ICML
Peng Wang, Huikang Liu, Druv Pai, Yaodong Yu, Zhihui Zhu, Qing Qu, Yi Ma
2024 J jnl
A Global Geometric Analysis of Maximal Coding Rate Reduction.
CoRR
Peng Wang, Huikang Liu, Druv Pai, Yaodong Yu, Zhihui Zhu, Qing Qu, Yi Ma
2024 — conf
A Study on the Calibration of In-context Learning.
NAACL-HLT
Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade
2024 J jnl
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation.
CoRR
Amartya Sanyal, Yaxi Hu, Yaodong Yu, Yian Ma, Yixin Wang, Bernhard Schölkopf
2024 J jnl
Causal Image Modeling for Efficient Visual Understanding.
CoRR
Feng Wang, Timing Yang, Yaodong Yu, Sucheng Ren, Guoyizhe Wei, Angtian Wang, Wei Shao, Yuyin Zhou, Alan L. Yuille, Cihang Xie
2024 A* conf
Differentially Private Representation Learning via Image Captioning.
ICML
Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Oliviero Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo
2024 J jnl
Differentially Private Representation Learning via Image Captioning.
CoRR
Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo
2024 — conf
Emergence of Segmentation with Minimalistic White-Box Transformers.
CPAL
Yaodong Yu, Tianzhe Chu, Shengbang Tong, Ziyang Wu, Druv Pai, Sam Buchanan, Yi Ma
2024 J jnl
M-VAR: Decoupled Scale-wise Autoregressive Modeling for High-Quality Image Generation.
CoRR
Sucheng Ren, Yaodong Yu, Nataniel Ruiz, Feng Wang, Alan L. Yuille, Cihang Xie
2024 A* conf
Masked Completion via Structured Diffusion with White-Box Transformers.
ICLR
Druv Pai, Sam Buchanan, Ziyang Wu, Yaodong Yu, Yi Ma
2024 J jnl
Masked Completion via Structured Diffusion with White-Box Transformers.
CoRR
Druv Pai, Ziyang Wu, Sam Buchanan, Yaodong Yu, Yi Ma
2024 — —
Reliable Representation Learning: Theory and Practice
Yaodong Yu
2024 A* conf
Scaling White-Box Transformers for Vision.
NeurIPS
Jinrui Yang, Xianhang Li, Druv Pai, Yuyin Zhou, Yi Ma, Yaodong Yu, Cihang Xie
2024 J jnl
Scaling White-Box Transformers for Vision.
CoRR
Jinrui Yang, Xianhang Li, Druv Pai, Yuyin Zhou, Yi Ma, Yaodong Yu, Cihang Xie
2024 J jnl
Scheduling a multi-agent flow shop with two scenarios and release dates.
Int. J. Prod. Res.
Xinyue Wang, Tao Ren, Danyu Bai, Feng Chu, Yaodong Yu, Fanchun Meng, Chin-Chia Wu
2024 J jnl
Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction.
CoRR
Ziyang Wu, Tianjiao Ding, Yifu Lu, Druv Pai, Jingyuan Zhang, Weida Wang, Yaodong Yu, Yi Ma, Benjamin D. Haeffele
2024 A* conf
ViP: A Differentially Private Foundation Model for Computer Vision.
ICML
Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo
2024 J jnl
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?
J. Mach. Learn. Res.
Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Hao Bai, Yuexiang Zhai, Benjamin D. Haeffele, Yi Ma
2023 J jnl
A Study on the Calibration of In-context Learning.
CoRR
Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade
2023 J jnl
Emergence of Segmentation with Minimalistic White-Box Transformers.
CoRR
Yaodong Yu, Tianzhe Chu, Shengbang Tong, Ziyang Wu, Druv Pai, Sam Buchanan, Yi Ma
2023 A* conf
Federated Conformal Predictors for Distributed Uncertainty Quantification.
ICML
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar
2023 J jnl
Federated Conformal Predictors for Distributed Uncertainty Quantification.
CoRR
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar
2023 J jnl
Scaff-PD: Communication Efficient Fair and Robust Federated Learning.
CoRR
Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
2023 J jnl
ViP: A Differentially Private Foundation Model for Computer Vision.
CoRR
Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo
2023 A* conf
White-Box Transformers via Sparse Rate Reduction.
NeurIPS
Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Benjamin D. Haeffele, Yi Ma
2023 J jnl
White-Box Transformers via Sparse Rate Reduction.
CoRR
Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Benjamin D. Haeffele, Yi Ma
2023 J jnl
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?
CoRR
Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Hao Bai, Yuexiang Zhai, Benjamin D. Haeffele, Yi Ma
2022 J jnl
CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction.
Entropy
Xili Dai, Shengbang Tong, Mingyang Li, Ziyang Wu, Michael Psenka, Kwan Ho Ryan Chan, Pengyuan Zhai, Yaodong Yu, Xiaojun Yuan, Heung-Yeung Shum, Yi Ma
2022 — conf
Conditional Supervised Contrastive Learning for Fair Text Classification.
EMNLP (Findings)
Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang, Han Zhao, Yuan Tian
2022 J jnl
Conditional Supervised Contrastive Learning for Fair Text Classification.
CoRR
Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang, Han Zhao, Yuan Tian
2022 A conf
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization.
AISTATS
Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan
2022 A conf
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging.
AISTATS
Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
2022 A* conf
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback.
ICML
Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan
2022 J jnl
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback.
CoRR
Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan
2022 A* conf
Predicting Out-of-Distribution Error with the Projection Norm.
ICML
Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, Jacob Steinhardt
2022 J jnl
Predicting Out-of-Distribution Error with the Projection Norm.
CoRR
Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, Jacob Steinhardt
2022 J jnl
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction.
J. Mach. Learn. Res.
Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma
2022 A* conf
Robust Calibration with Multi-domain Temperature Scaling.
NeurIPS
Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan
2022 J jnl
Robust Calibration with Multi-domain Temperature Scaling.
CoRR
Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan
2022 A* conf
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels.
NeurIPS
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
2022 J jnl
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels.
CoRR
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
2022 J jnl
What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning.
CoRR
Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jaroslaw Blasiok, Preetum Nakkiran
2022 A* conf
What You See is What You Get: Principled Deep Learning via Distributional Generalization.
NeurIPS
Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jaroslaw Blasiok, Preetum Nakkiran
2021 — conf
Adversarial Robustness of Stabilized Neural ODE Might be from Obfuscated Gradients.
MSML
Yifei Huang, Yaodong Yu, Hongyang Zhang, Yi Ma, Yuan Yao
2021 J jnl
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction.
CoRR
Xili Dai, Shengbang Tong, Mingyang Li, Ziyang Wu, Kwan Ho Ryan Chan, Pengyuan Zhai, Yaodong Yu, Michael Psenka, Xiaojun Yuan, Heung-Yeung Shum, Yi Ma
2021 J jnl
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization.
CoRR
Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan
2021 J jnl
On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging.
CoRR
Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
2021 J jnl
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction.
CoRR
Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma
2021 J jnl
The Effect of Model Size on Worst-Group Generalization.
CoRR
Alan Pham, Eunice Chan, Vikranth Srivatsa, Dhruba Ghosh, Yaoqing Yang, Yaodong Yu, Ruiqi Zhong, Joseph E. Gonzalez, Jacob Steinhardt
2021 J jnl
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition.
CoRR
Yaodong Yu, Zitong Yang, Edgar Dobriban, Jacob Steinhardt, Yi Ma
2020 J jnl
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients.
CoRR
Yifei Huang, Yaodong Yu, Hongyang Zhang, Yi Ma, Yuan Yao
2020 A* conf
Boundary thickness and robustness in learning models.
NeurIPS
Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney
2020 J jnl
Boundary thickness and robustness in learning models.
CoRR
Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney
2020 J jnl
Deep Networks from the Principle of Rate Reduction.
CoRR
Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma
2020 A* conf
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction.
NeurIPS
Yaodong Yu, Kwan Ho Ryan Chan, Chong You, Chaobing Song, Yi Ma
2020 J jnl
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction.
CoRR
Yaodong Yu, Kwan Ho Ryan Chan, Chong You, Chaobing Song, Yi Ma
2020 A* conf
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks.
ICML
Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma
2020 J jnl
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks.
CoRR
Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma
2019 A conf
Learning One-hidden-layer ReLU Networks via Gradient Descent.
AISTATS
Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu
2019 A* conf
Theoretically Principled Trade-off between Robustness and Accuracy.
ICML
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan
2019 J jnl
Theoretically Principled Trade-off between Robustness and Accuracy.
CoRR
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan
2018 A* conf
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery.
ICML
Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu
2018 A* conf
Data Poisoning Attacks on Multi-Task Relationship Learning.
AAAI
Mengchen Zhao, Bo An, Yaodong Yu, Sulin Liu, Sinno Jialin Pan
2018 J jnl
Learning One-hidden-layer ReLU Networks via Gradient Descent.
CoRR
Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu
2018 A* conf
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima.
NeurIPS
Yaodong Yu, Pan Xu, Quanquan Gu
2017 A conf
Communication-Efficient Distributed Primal-Dual Algorithm for Saddle Point Problem.
UAI
Yaodong Yu, Sulin Liu, Sinno Jialin Pan
2017 J jnl
Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently.
CoRR
Yaodong Yu, Difan Zou, Quanquan Gu
2017 J jnl
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima.
CoRR
Yaodong Yu, Pan Xu, Quanquan Gu