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Yaodong Yu
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2020 – today
- 2024
- [j3]Xinyue Wang
, Tao Ren, Danyu Bai
Scheduling a multi-agent flow shop with two scenarios and release dates. Int. J. Prod. Res. 62(1-2): 421-443 (2024) - [c25]Druv Pai, Sam Buchanan, Ziyang Wu, Yaodong Yu, Yi Ma:
Masked Completion via Structured Diffusion with White-Box Transformers. ICLR 2024 - [c24]Peng Wang, Huikang Liu, Druv Pai, Yaodong Yu, Zhihui Zhu, Qing Qu
A Global Geometric Analysis of Maximal Coding Rate Reduction. ICML 2024 - [c23]Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Oliviero Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
Differentially Private Representation Learning via Image Captioning. ICML 2024 - [c22]Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
ViP: A Differentially Private Foundation Model for Computer Vision. ICML 2024 - [c21]Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade:
A Study on the Calibration of In-context Learning. NAACL-HLT 2024: 6118-6136 - [i33]Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
Differentially Private Representation Learning via Image Captioning. CoRR abs/2403.02506 (2024) - [i32]Druv Pai, Ziyang Wu, Sam Buchanan, Yaodong Yu, Yi Ma:
Masked Completion via Structured Diffusion with White-Box Transformers. CoRR abs/2404.02446 (2024) - [i31]Jinrui Yang, Xianhang Li, Druv Pai, Yuyin Zhou, Yi Ma, Yaodong Yu, Cihang Xie:
Scaling White-Box Transformers for Vision. CoRR abs/2405.20299 (2024) - [i30]Peng Wang, Huikang Liu, Druv Pai, Yaodong Yu, Zhihui Zhu, Qing Qu, Yi Ma:
A Global Geometric Analysis of Maximal Coding Rate Reduction. CoRR abs/2406.01909 (2024) - [i29]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 abs/2406.19049 (2024) - 2023
- [c20]Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar:
Federated Conformal Predictors for Distributed Uncertainty Quantification. ICML 2023: 22942-22964 - [c19]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 - [i28]Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar:
Federated Conformal Predictors for Distributed Uncertainty Quantification. CoRR abs/2305.17564 (2023) - [i27]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 abs/2306.01129 (2023) - [i26]Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
ViP: A Differentially Private Foundation Model for Computer Vision. CoRR abs/2306.08842 (2023) - [i25]Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan:
Scaff-PD: Communication Efficient Fair and Robust Federated Learning. CoRR abs/2307.13381 (2023) - [i24]Yaodong Yu, Tianzhe Chu, Shengbang Tong, Ziyang Wu, Druv Pai, Sam Buchanan, Yi Ma:
Emergence of Segmentation with Minimalistic White-Box Transformers. CoRR abs/2308.16271 (2023) - [i23]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 abs/2311.13110 (2023) - [i22]Hanlin Zhang, Yi-Fan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade:
A Study on the Calibration of In-context Learning. CoRR abs/2312.04021 (2023) - 2022
- [j2]Xili Dai, Shengbang Tong, Mingyang Li, Ziyang Wu, Michael Psenka, Kwan Ho Ryan Chan, Pengyuan Zhai, Yaodong Yu
CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction. Entropy 24(4): 456 (2022) - [j1]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: 114:1-114:103 (2022) - [c18]Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. AISTATS 2022: 1219-1250 - [c17]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: 9793-9826 - [c16]Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang, Han Zhao, Yuan Tian:
Conditional Supervised Contrastive Learning for Fair Text Classification. EMNLP (Findings) 2022: 2736-2756 - [c15]Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan:
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. ICML 2022: 13441-13467 - [c14]Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, Jacob Steinhardt:
Predicting Out-of-Distribution Error with the Projection Norm. ICML 2022: 25721-25746 - [c13]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 - [c12]Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan:
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. NeurIPS 2022 - [c11]Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan:
Robust Calibration with Multi-domain Temperature Scaling. NeurIPS 2022 - [i21]Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, Jacob Steinhardt:
Predicting Out-of-Distribution Error with the Projection Norm. CoRR abs/2202.05834 (2022) - [i20]Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jaroslaw Blasiok
What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning. CoRR abs/2204.03230 (2022) - [i19]Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan:
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. CoRR abs/2205.07217 (2022) - [i18]Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang, Han Zhao, Yuan Tian:
Conditional Supervised Contrastive Learning for Fair Text Classification. CoRR abs/2205.11485 (2022) - [i17]Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan:
Robust Calibration with Multi-domain Temperature Scaling. CoRR abs/2206.02757 (2022) - [i16]Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan:
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. CoRR abs/2207.06343 (2022) - 2021
- [c10]Yifei Huang, Yaodong Yu, Hongyang Zhang, Yi Ma, Yuan Yao:
Adversarial Robustness of Stabilized Neural ODE Might be from Obfuscated Gradients. MSML 2021: 497-515 - [i15]Yaodong Yu, Zitong Yang, Edgar Dobriban, Jacob Steinhardt, Yi Ma:
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition. CoRR abs/2103.09947 (2021) - [i14]Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization. CoRR abs/2104.13326 (2021) - [i13]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 abs/2105.10446 (2021) - [i12]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 abs/2107.00464 (2021) - [i11]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 abs/2111.06636 (2021) - [i10]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 abs/2112.04094 (2021) - 2020
- [c9]Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma:
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks. ICML 2020: 10767-10777 - [c8]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 - [c7]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 - [i9]Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, Yi Ma:
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks. CoRR abs/2002.11328 (2020) - [i8]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 abs/2006.08558 (2020) - [i7]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 abs/2007.05086 (2020) - [i6]Yifei Huang, Yaodong Yu, Hongyang Zhang, Yi Ma, Yuan Yao:
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients. CoRR abs/2009.13145 (2020) - [i5]Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma:
Deep Networks from the Principle of Rate Reduction. CoRR abs/2010.14765 (2020)
2010 – 2019
- 2019
- [c6]Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu:
Learning One-hidden-layer ReLU Networks via Gradient Descent. AISTATS 2019: 1524-1534 - [c5]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: 7472-7482 - [i4]Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan:
Theoretically Principled Trade-off between Robustness and Accuracy. CoRR abs/1901.08573 (2019) - 2018
- [c4]Mengchen Zhao, Bo An, Yaodong Yu, Sulin Liu, Sinno Jialin Pan:
Data Poisoning Attacks on Multi-Task Relationship Learning. AAAI 2018: 2628-2635 - [c3]Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu:
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery. ICML 2018: 5857-5866 - [c2]Yaodong Yu, Pan Xu, Quanquan Gu:
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima. NeurIPS 2018: 4530-4540 - [i3]Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu:
Learning One-hidden-layer ReLU Networks via Gradient Descent. CoRR abs/1806.07808 (2018) - 2017
- [c1]Yaodong Yu, Sulin Liu, Sinno Jialin Pan:
Communication-Efficient Distributed Primal-Dual Algorithm for Saddle Point Problem. UAI 2017 - [i2]Yaodong Yu, Difan Zou, Quanquan Gu:
Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently. CoRR abs/1712.03950 (2017) - [i1]Yaodong Yu, Pan Xu, Quanquan Gu:
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima. CoRR abs/1712.06585 (2017)
Coauthor Index
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last updated on 2024-11-08 20:30 CET by the dblp team
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