67 citations · 183 across the 15 of their papers we have counts for
15 papers
Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation
Changchang Yin, Hong-You Chen, Wei-Lun Chao +1
Individual treatment effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucial problem in healthcare. Most ex…
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction
Ziwei Li, Xiaoqi Wang, Hong-You Chen +2
Federated learning (FL) has rapidly evolved as a promising paradigm that enables collaborative model training across distributed participants without exchanging their local data. D…
Fine-Tuning is Fine, if Calibrated
Zheda Mai, Arpita Chowdhury, Ping Zhang +8
Fine-tuning is arguably the most straightforward way to tailor a pre-trained model (e.g., a foundation model) to downstream applications, but it also comes with the risk of losing…
Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition
Zheda Mai, Ping Zhang, Cheng-Hao Tu +3
Parameter-efficient fine-tuning (PEFT) has attracted significant attention due to the growth of pre-trained model sizes and the need to fine-tune (FT) them for superior downstream…
Jigsaw Game: Federated Clustering
Jinxuan Xu, Hong-You Chen, Wei-Lun Chao +1
Federated learning has recently garnered significant attention, especially within the domain of supervised learning. However, despite the abundance of unlabeled data on end-users,…
Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs
Vardaan Pahuja, Weidi Luo, Yu Gu +7
Camera traps are important tools in animal ecology for biodiversity monitoring and conservation. However, their practical application is limited by issues such as poor generalizati…
Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data
Cheng-Hao Tu, Hong-You Chen, Zheda Mai +7
We propose a learning problem involving adapting a pre-trained source model to the target domain for classifying all classes that appeared in the source data, using target data tha…
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification
Hong-You Chen, Jike Zhong, Mingda Zhang +5
Personalized federated learning (PFL) aims to harness the collective wisdom of clients' data while building personalized models tailored to individual clients' data distributions.…
Learning Fractals by Gradient Descent
Cheng-Hao Tu, Hong-You Chen, David Carlyn +1
Fractals are geometric shapes that can display complex and self-similar patterns found in nature (e.g., clouds and plants). Recent works in visual recognition have leveraged this p…
Making Batch Normalization Great in Federated Deep Learning
Jike Zhong, Hong-You Chen, Wei-Lun Chao
Batch Normalization (BN) is widely used in {centralized} deep learning to improve convergence and generalization. However, in {federated} learning (FL) with decentralized data, pri…
Gradual Domain Adaptation without Indexed Intermediate Domains
Hong-You Chen, Wei-Lun Chao
The effectiveness of unsupervised domain adaptation degrades when there is a large discrepancy between the source and target domains. Gradual domain adaptation (GDA) is one promisi…
On the Importance and Applicability of Pre-Training for Federated Learning
Hong-You Chen, Cheng-Hao Tu, Ziwei Li +2
Pre-training is prevalent in nowadays deep learning to improve the learned model's performance. However, in the literature on federated learning (FL), neural networks are mostly in…
On Bridging Generic and Personalized Federated Learning for Image Classification
Hong-You Chen, Wei-Lun Chao
Federated learning is promising for its capability to collaboratively train models with multiple clients without accessing their data, but vulnerable when clients' data distributio…
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
Hong-You Chen, Wei-Lun Chao
Federated learning aims to collaboratively train a strong global model by accessing users' locally trained models but not their own data. A crucial step is therefore to aggregate l…
Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning
Han-Jia Ye, Hong-You Chen, De-Chuan Zhan +1
Classifiers trained with class-imbalanced data are known to perform poorly on test data of the "minor" classes, of which we have insufficient training data. In this paper, we inves…