Jiayi Shen (沈佳怡)

I am a final year ELLIS PhD candidate at MultiX Lab, University of Amsterdam. I am grateful to be supervised by Prof. Dr. Marcel Worring and Dr. Nanne van Noord. I also received valuable guidance from Dr. Xiantong Zhen. Prior to that, I obtained my Master's and Bachelor's degrees from Beihang University.

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[Sep. 2024] One paper was accepted by NeurIPS 2024!

[June 2024] I am happy to give a talk "Probabilistic Modeling for Knowledge Transfer" for Yingzhen's group at Imperial College London.

[Feb. 2024] One paper on adapting foundation models was accepted by CVPR 2024.

[Feb. 2024] One tutorial on "How to Improve Your Academic Reading Skills" in MultiX. Slides can be found HERE!

I'm interested in Machine Learning, Bayesian Models, and Meta-Learning. Much of my research is to address challenging problems in multi-task learning.

This paper proposes GO4Align, a multi-task optimization approach that tackles task imbalance by explicitly aligning the optimization across tasks.

We propose any-shift prompting, a general probabilistic inference framework that considers both the training and test task information for prompt generation, to improve the generalization on downstream tasks across various distribution shifts by utilizing the original generalization ability.

This paper focuses on the data-insufficiency problem in multi-task learning within an episodic training set-up.

We propose SuperDisco to discover super-class representations for long-tailed recognition using a graph model.

We propose a new MTL setting which suffers from category shifts from training to test data.

We propose a Neighbor Transformer Network, or NFormer, which explicitly models interactions across all input images.

We propose variational multi-task learning (VMTL), a general probabilistic inference framework for learning multiple related tasks.

To better explore domain invariant learning, we introduce weight uncertainty to the model by leveraging variational Bayesian inference.