Yaodong Yu

According to our database¹, Yaodong Yu authored at least 38 papers between 2017 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

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On csauthors.net:

Bibliography

2024

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

[BibT_eX]

[DOI]

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

Differentially Private Representation Learning via Image Captioning.

[BibT_eX]

[DOI]

CoRR, 2024

2023

A Study on the Calibration of In-context Learning.

[BibT_eX]

[DOI]

CoRR, 2023

White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?

[BibT_eX]

[DOI]

CoRR, 2023

Emergence of Segmentation with Minimalistic White-Box Transformers.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

Yaodong Yu

Sai Praneeth Karimireddy

Yi Ma

Michael I. Jordan

CoRR, 2023

ViP: A Differentially Private Foundation Model for Computer Vision.

[BibT_eX]

[DOI]

CoRR, 2023

White-Box Transformers via Sparse Rate Reduction.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction.

[BibT_eX]

[DOI]

Entropy, 2022

What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Robust Calibration with Multi-domain Temperature Scaling.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

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

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Conditional Supervised Contrastive Learning for Fair Text Classification.

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

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

2021

The Effect of Model Size on Worst-Group Generalization.

[BibT_eX]

[DOI]

CoRR, 2021

Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction.

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

Proceedings of the Mathematical and Scientific Machine Learning, 2021

2020

Deep Networks from the Principle of Rate Reduction.

[BibT_eX]

[DOI]

CoRR, 2020

Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients.

[BibT_eX]

[DOI]

CoRR, 2020

Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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

2019

Theoretically Principled Trade-off between Robustness and Accuracy.

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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

Data Poisoning Attacks on Multi-Task Relationship Learning.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Yaodong Yu

Pan Xu

Quanquan Gu

CoRR, 2017

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

[BibT_eX]

[DOI]

Yaodong Yu

Difan Zou

Quanquan Gu

CoRR, 2017

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

[BibT_eX]

[DOI]

Yaodong Yu

Sulin Liu

Sinno Jialin Pan

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

Yaodong Yu

Timeline

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On csauthors.net:

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