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Hong-You Chen

Orcid: 0009-0001-6408-9097

According to our database¹, Hong-You Chen authored at least 37 papers between 2018 and 2026.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book In proceedings Article PhD thesis Dataset Other

Links

On csauthors.net:

Bibliography

2026

Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation.

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ACM Trans. Intell. Syst. Technol., April, 2026

DREAM: Where Visual Understanding Meets Text-to-Image Generation.

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Sai Vidyaranya Nuthalapati

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Satya Narayan Shukla

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Shlok Kumar Mishra

CoRR, March, 2026

Reason to Contrast: A Cascaded Multimodal Retrieval Framework.

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Shlok Kumar Mishra

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CoRR, February, 2026

Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendation.

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CoRR, February, 2026

Enhancement-Mode Lattice-Matched AlInN/GaN/AlGaN/GaN Heterostructured-Fin-Gated Multichannel and Double-Channel MOSHEMTs.

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IEEE Access, 2026

2025

Contrastive Localized Language-Image Pre-Training.

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Proceedings of the Forty-second International Conference on Machine Learning, 2025

MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning.

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Aleksei Timofeev

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Jean-Philippe Fauconnier

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et al.

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models.

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Vasileios Saveris

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Proceedings of the Thirteenth International Conference on Learning Representations, 2025

CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling.

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Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2025, 2025

Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition.

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Quang-Huy Nguyen

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

2024

Jigsaw Game: Federated Clustering.

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Trans. Mach. Learn. Res., 2024

MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning.

[DOI]

CoRR, 2024

Lessons Learned from a Unifying Empirical Study of Parameter-Efficient Transfer Learning (PETL) in Visual Recognition.

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CoRR, 2024

SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models.

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CoRR, 2024

Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models.

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William Yang Wang

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CoRR, 2024

Bringing Back the Context: Camera Trap Species Identification as Link Prediction on Multimodal Knowledge Graphs.

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Tanya Y. Berger-Wolf

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Charles V. Stewart

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CoRR, 2024

Fine-Tuning is Fine, if Calibrated.

[DOI]

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Arpita Chowdhury

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Tanya Y. Berger-Wolf

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Charles V. Stewart

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Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction.

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Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs.

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Tanya Y. Berger-Wolf

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Charles V. Stewart

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Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2023

Federated Learning of Shareable Bases for Personalization-Friendly Image Classification.

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CoRR, 2023

Making Batch Normalization Great in Federated Deep Learning.

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CoRR, 2023

Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data.

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Tanya Y. Berger-Wolf

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Charles V. Stewart

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Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Importance and Applicability of Pre-Training for Federated Learning.

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Proceedings of the Eleventh International Conference on Learning Representations, 2023

Train-Once-for-All Personalization.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Learning Fractals by Gradient Descent.

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Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

On Pre-Training for Federated Learning.

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CoRR, 2022

On Bridging Generic and Personalized Federated Learning for Image Classification.

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Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

On Bridging Generic and Personalized Federated Learning.

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CoRR, 2021

Gradual Domain Adaptation without Indexed Intermediate Domains.

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Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning.

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Proceedings of the 9th International Conference on Learning Representations, 2021

2020

FedDistill: Making Bayesian Model Ensemble Applicable to Federated Learning.

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CoRR, 2020

Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning.

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CoRR, 2020

Glyph2Vec: Learning Chinese Out-of-Vocabulary Word Embedding from Glyphs.

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Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019

Self-Discriminative Learning for Unsupervised Document Embedding.

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Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

Nested Variance Estimating VAE/GAN for Face Generation.

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Proceedings of the International Joint Conference on Neural Networks, 2019

Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training.

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Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

2018

Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings.

[DOI]

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Cheng-Syuan Lee

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Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

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