Yaodong Yu | DeepAI

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∙ 08/30/2023

Emergence of Segmentation with Minimalistic White-Box Transformers

Transformer-like models for vision tasks have recently proven effective ...

0 Yaodong Yu, et al. ∙

∙ 07/25/2023

Scaff-PD: Communication Efficient Fair and Robust Federated Learning

We present Scaff-PD, a fast and communication-efficient algorithm for di...

0 Yaodong Yu, et al. ∙

∙ 06/15/2023

ViP: A Differentially Private Foundation Model for Computer Vision

Artificial intelligence (AI) has seen a tremendous surge in capabilities...

0 Yaodong Yu, et al. ∙

∙ 06/01/2023

White-Box Transformers via Sparse Rate Reduction

In this paper, we contend that the objective of representation learning ...

0 Yaodong Yu, et al. ∙

∙ 05/27/2023

Federated Conformal Predictors for Distributed Uncertainty Quantification

Conformal prediction is emerging as a popular paradigm for providing rig...

0 Charles Lu, et al. ∙

∙ 07/13/2022

TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels

State-of-the-art federated learning methods can perform far worse than t...

5 Yaodong Yu, et al. ∙

∙ 05/23/2022

Conditional Supervised Contrastive Learning for Fair Text Classification

Contrastive representation learning has gained much attention due to its...

0 Jianfeng Chi, et al. ∙

∙ 05/15/2022

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

Motivated by applications to online learning in sparse estimation and Ba...

6 Tianyi Lin, et al. ∙

∙ 04/07/2022

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

We investigate and leverage a connection between Differential Privacy (D...

7 Bogdan Kulynych, et al. ∙

∙ 02/11/2022

Predicting Out-of-Distribution Error with the Projection Norm

We propose a metric – Projection Norm – to predict a model's performance...

10 Yaodong Yu, et al. ∙

∙ 12/08/2021

The Effect of Model Size on Worst-Group Generalization

Overparameterization is shown to result in poor test accuracy on rare su...

4 Alan Pham, et al. ∙

∙ 11/12/2021

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

This work proposes a new computational framework for learning an explici...

1 Xili Dai, et al. ∙

∙ 06/30/2021

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

We study the stochastic bilinear minimax optimization problem, presentin...

1 Chris Junchi Li, et al. ∙

∙ 05/21/2021

ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction

This work attempts to provide a plausible theoretical framework that aim...

16 Kwan Ho Ryan Chan, et al. ∙

∙ 04/27/2021

Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization

Distributionally robust supervised learning (DRSL) is emerging as a key ...

18 Yaodong Yu, et al. ∙

∙ 03/17/2021

Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition

Adversarially trained models exhibit a large generalization gap: they ca...

8 Yaodong Yu, et al. ∙

∙ 10/27/2020

Deep Networks from the Principle of Rate Reduction

This work attempts to interpret modern deep (convolutional) networks fro...

16 Kwan Ho Ryan Chan, et al. ∙

∙ 07/09/2020

Boundary thickness and robustness in learning models

Robustness of machine learning models to various adversarial and non-adv...

14 Yaoqing Yang, et al. ∙

∙ 06/15/2020

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

To learn intrinsic low-dimensional structures from high-dimensional data...

0 Yaodong Yu, et al. ∙

∙ 02/26/2020

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

The classical bias-variance trade-off predicts that bias decreases and v...

0 Zitong Yang, et al. ∙

∙ 01/24/2019

Theoretically Principled Trade-off between Robustness and Accuracy

We identify a trade-off between robustness and accuracy that serves as a...

5 Hongyang Zhang, et al. ∙

∙ 06/20/2018

Learning One-hidden-layer ReLU Networks via Gradient Descent

We study the problem of learning one-hidden-layer neural networks with R...

2 Xiao Zhang, et al. ∙

∙ 12/18/2017

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

We propose stochastic optimization algorithms that can find local minima...

0 Yaodong Yu, et al. ∙

∙ 12/11/2017

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

We propose a family of nonconvex optimization algorithms that are able t...

0 Yaodong Yu, et al. ∙

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