Yaodong Yu

According to our database, Yaodong Yu authored at least 43 papers between 2017 and 2024.

Collaborative distances:

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

2024

Scheduling a multi-agent flow shop with two scenarios and release dates.

Xinyue Wang, Tao Ren, Danyu Bai, Feng Chu, Yaodong Yu, Fanchun Meng, Chin-Chia Wu

Int. J. Prod. Res., January, 2024

Causal Image Modeling for Efficient Visual Understanding.

Feng Wang, Timing Yang, Yaodong Yu, Sucheng Ren, Guoyizhe Wei, Angtian Wang, Wei Shao, Yuyin Zhou, Alan L. Yuille, Cihang Xie

CoRR, 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

CoRR, 2024

Scaling White-Box Transformers for Vision.

Jinrui Yang, Xianhang Li, Druv Pai, Yuyin Zhou, Yi Ma, Yaodong Yu, Cihang Xie

CoRR, 2024

A Study on the Calibration of In-context Learning.

Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

ViP: A Differentially Private Foundation Model for Computer Vision.

Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Differentially Private Representation Learning via Image Captioning.

Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Oliviero Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Global Geometric Analysis of Maximal Coding Rate Reduction.

Peng Wang, Huikang Liu, Druv Pai, Yaodong Yu, Zhihui Zhu, Qing Qu, Yi Ma

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Masked Completion via Structured Diffusion with White-Box Transformers.

Druv Pai, Sam Buchanan, Ziyang Wu, Yaodong Yu, Yi Ma

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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

CoRR, 2023

Emergence of Segmentation with Minimalistic White-Box Transformers.

Yaodong Yu, Tianzhe Chu, Shengbang Tong, Ziyang Wu, Druv Pai, Sam Buchanan, Yi Ma

CoRR, 2023

Scaff-PD: Communication Efficient Fair and Robust Federated Learning.

Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Federated Conformal Predictors for Distributed Uncertainty Quantification.

Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar

Proceedings of the International Conference on Machine Learning, 2023

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, Yi Ma

J. Mach. Learn. Res., 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, Heung-Yeung Shum, Yi Ma

Entropy, 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

Robust Calibration with Multi-domain Temperature Scaling.

Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels.

Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Predicting Out-of-Distribution Error with the Projection Norm.

Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, Jacob Steinhardt

Proceedings of the International Conference on Machine Learning, 2022

Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback.

Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan

Proceedings of the International Conference on Machine Learning, 2022

Conditional Supervised Contrastive Learning for Fair Text Classification.

Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang, Han Zhao, Yuan Tian

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization.

Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging.

Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

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

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, Heung-Yeung Shum, Yi Ma

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, Nicolas Le Roux, Michael I. Jordan

CoRR, 2021

Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition.

Yaodong Yu, Zitong Yang, Edgar Dobriban, Jacob Steinhardt, Yi Ma

CoRR, 2021

Adversarial Robustness of Stabilized Neural ODE Might be from Obfuscated Gradients.

Yifei Huang, Yaodong Yu, Hongyang Zhang, Yi Ma, Yuan Yao

Proceedings of the Mathematical and Scientific Machine Learning, 2021

2020

Deep Networks from the Principle of Rate Reduction.

Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma

CoRR, 2020

Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients.

Yifei Huang, Yaodong Yu, Hongyang Zhang, Yi Ma, Yuan Yao

CoRR, 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

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

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

Rethinking Bias-Variance Trade-off for Generalization of Neural Networks.

Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma

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

2019

Theoretically Principled Trade-off between Robustness and Accuracy.

Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan

Proceedings of the 36th International Conference on Machine Learning, 2019

Learning One-hidden-layer ReLU Networks via Gradient Descent.

Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima.

Yaodong Yu, Pan Xu, Quanquan Gu

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

A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery.

Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu

Proceedings of the 35th International Conference on Machine Learning, 2018

Data Poisoning Attacks on Multi-Task Relationship Learning.

Mengchen Zhao, Bo An, Yaodong Yu, Sulin Liu, Sinno Jialin Pan

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima.

Yaodong Yu, Pan Xu, Quanquan Gu

CoRR, 2017

Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently.

Yaodong Yu, Difan Zou, Quanquan Gu

CoRR, 2017

Communication-Efficient Distributed Primal-Dual Algorithm for Saddle Point Problem.

Yaodong Yu, Sulin Liu, Sinno Jialin Pan

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017