Yaodong Yu

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 58

Top Topics

Differentially Private

Stochastic Optimization

Top Venues

Publications

Druv Pai, Sam Buchanan, Ziyang Wu, Yaodong Yu, Yi Ma
Masked Completion via Structured Diffusion with White-Box Transformers. ICLR (2024)
Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo
Differentially Private Representation Learning via Image Captioning. CoRR (2024)
Peng Wang, Huikang Liu, Druv Pai, Yaodong Yu, Zhihui Zhu, Qing Qu, Yi Ma
A Global Geometric Analysis of Maximal Coding Rate Reduction. CoRR (2024)
Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade
A Study on the Calibration of In-context Learning. NAACL-HLT (2024)
Amartya Sanyal, Yaxi Hu, Yaodong Yu, Yian Ma, Yixin Wang, Bernhard Schölkopf
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation. CoRR (2024)
Jinrui Yang, Xianhang Li, Druv Pai, Yuyin Zhou, Yi Ma, Yaodong Yu, Cihang Xie
Scaling White-Box Transformers for Vision. CoRR (2024)
Xinyue Wang, Tao Ren, Danyu Bai, Feng Chu, Yaodong Yu, Fanchun Meng, Chin-Chia Wu
Scheduling a multi-agent flow shop with two scenarios and release dates. Int. J. Prod. Res. 62 (1-2) (2024)
Druv Pai, Ziyang Wu, Sam Buchanan, Yaodong Yu, Yi Ma
Masked Completion via Structured Diffusion with White-Box Transformers. CoRR (2024)
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar
Federated Conformal Predictors for Distributed Uncertainty Quantification. CoRR (2023)
Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo
ViP: A Differentially Private Foundation Model for Computer Vision. CoRR (2023)
Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
Scaff-PD: Communication Efficient Fair and Robust Federated Learning. CoRR (2023)
Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Hao Bai, Yuexiang Zhai, Benjamin D. Haeffele, Yi Ma
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is? CoRR (2023)
Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Benjamin D. Haeffele, Yi Ma
White-Box Transformers via Sparse Rate Reduction. NeurIPS (2023)
Yaodong Yu, Tianzhe Chu, Shengbang Tong, Ziyang Wu, Druv Pai, Sam Buchanan, Yi Ma
Emergence of Segmentation with Minimalistic White-Box Transformers. CoRR (2023)
Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Benjamin D. Haeffele, Yi Ma
White-Box Transformers via Sparse Rate Reduction. CoRR (2023)
Hanlin Zhang, Yi-Fan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, Eric P. Xing, Hima Lakkaraju, Sham M. Kakade
A Study on the Calibration of In-context Learning. CoRR (2023)
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar
Federated Conformal Predictors for Distributed Uncertainty Quantification. ICML (2023)
Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction. J. Mach. Learn. Res. 23 (2022)
Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jaroslaw Blasiok, Preetum Nakkiran
What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning. CoRR (2022)
Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. ICML (2022)
Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, Jacob Steinhardt
Predicting Out-of-Distribution Error with the Projection Norm. ICML (2022)
Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. CoRR (2022)
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. CoRR (2022)
Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging. AISTATS (2022)
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. NeurIPS (2022)
Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan
Robust Calibration with Multi-domain Temperature Scaling. NeurIPS (2022)
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
CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction. Entropy 24 (4) (2022)
Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang, Han Zhao, Yuan Tian
Conditional Supervised Contrastive Learning for Fair Text Classification. CoRR (2022)
Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan
Robust Calibration with Multi-domain Temperature Scaling. CoRR (2022)
Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jaroslaw Blasiok, Preetum Nakkiran
What You See is What You Get: Principled Deep Learning via Distributional Generalization. NeurIPS (2022)
Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, Jacob Steinhardt
Predicting Out-of-Distribution Error with the Projection Norm. CoRR (2022)
Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. AISTATS (2022)
Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang, Han Zhao, Yuan Tian
Conditional Supervised Contrastive Learning for Fair Text Classification. EMNLP (Findings) (2022)
Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization. CoRR (2021)
Alan Pham, Eunice Chan, Vikranth Srivatsa, Dhruba Ghosh, Yaoqing Yang, Yaodong Yu, Ruiqi Zhong, Joseph E. Gonzalez, Jacob Steinhardt
The Effect of Model Size on Worst-Group Generalization. CoRR (2021)
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
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction. CoRR (2021)
Yifei Huang, Yaodong Yu, Hongyang Zhang, Yi Ma, Yuan Yao
Adversarial Robustness of Stabilized Neural ODE Might be from Obfuscated Gradients. MSML (2021)
Yaodong Yu, Zitong Yang, Edgar Dobriban, Jacob Steinhardt, Yi Ma
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition. CoRR (2021)
Chris Junchi Li, Yaodong Yu, Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux, Michael I. Jordan
On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging. CoRR (2021)
Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction. CoRR (2021)
Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney
Boundary thickness and robustness in learning models. CoRR (2020)
Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks. ICML (2020)
Yaodong Yu, Kwan Ho Ryan Chan, Chong You, Chaobing Song, Yi Ma
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction. NeurIPS (2020)
Yaodong Yu, Kwan Ho Ryan Chan, Chong You, Chaobing Song, Yi Ma
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction. CoRR (2020)
Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks. CoRR (2020)
Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney
Boundary thickness and robustness in learning models. NeurIPS (2020)
Yifei Huang, Yaodong Yu, Hongyang Zhang, Yi Ma, Yuan Yao
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients. CoRR (2020)
Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma
Deep Networks from the Principle of Rate Reduction. CoRR (2020)
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan
Theoretically Principled Trade-off between Robustness and Accuracy. CoRR (2019)
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan
Theoretically Principled Trade-off between Robustness and Accuracy. ICML (2019)
Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu
Learning One-hidden-layer ReLU Networks via Gradient Descent. AISTATS (2019)
Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu
Learning One-hidden-layer ReLU Networks via Gradient Descent. CoRR (2018)
Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery. ICML (2018)
Yaodong Yu, Pan Xu, Quanquan Gu
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima. NeurIPS (2018)
Mengchen Zhao, Bo An, Yaodong Yu, Sulin Liu, Sinno Jialin Pan
Data Poisoning Attacks on Multi-Task Relationship Learning. AAAI (2018)
Yaodong Yu, Pan Xu, Quanquan Gu
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima. CoRR (2017)
Yaodong Yu, Sulin Liu, Sinno Jialin Pan
Communication-Efficient Distributed Primal-Dual Algorithm for Saddle Point Problem. UAI (2017)
Yaodong Yu, Difan Zou, Quanquan Gu
Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently. CoRR (2017)