Search Results for author: Le Peng

Found 8 papers, 3 papers with code

Federated Learning with Convex Global and Local Constraints

no code implementations16 Oct 2023 Chuan He, Le Peng, Ju Sun

In practice, many machine learning (ML) problems come with constraints, and their applied domains involve distributed sensitive data that cannot be shared with others, e. g., in healthcare.

Fairness Federated Learning

Optimization and Optimizers for Adversarial Robustness

no code implementations23 Mar 2023 Hengyue Liang, Buyun Liang, Le Peng, Ying Cui, Tim Mitchell, Ju Sun

Taking advantage of PWCF and other existing numerical algorithms, we further explore the distinct patterns in the solutions found for solving these optimization problems using various combinations of losses, perturbation models, and optimization algorithms.

Adversarial Robustness

Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective

no code implementations17 Feb 2023 Yash Travadi, Le Peng, Xuan Bi, Ju Sun, Mochen Yang

However, the economic considerations of the clients, such as fairness and incentive, are yet to be fully explored.

Distributed Computing Fairness +2

Imbalanced Classification in Medical Imaging via Regrouping

no code implementations21 Oct 2022 Le Peng, Yash Travadi, Rui Zhang, Ying Cui, Ju Sun

We propose performing imbalanced classification by regrouping majority classes into small classes so that we turn the problem into balanced multiclass classification.

Image Classification imbalanced classification +1

Self-Validation: Early Stopping for Single-Instance Deep Generative Priors

2 code implementations23 Oct 2021 Taihui Li, Zhong Zhuang, Hengyue Liang, Le Peng, Hengkang Wang, Ju Sun

Recent works have shown the surprising effectiveness of deep generative models in solving numerous image reconstruction (IR) tasks, even without training data.

Image Reconstruction

Rethinking Transfer Learning for Medical Image Classification

2 code implementations9 Jun 2021 Le Peng, Hengyue Liang, Gaoxiang Luo, Taihui Li, Ju Sun

Transfer learning (TL) from pretrained deep models is a standard practice in modern medical image classification (MIC).

Image Classification Medical Image Classification +1

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