About Me

I am a graduate student pursuing a PhD's degree in the Department of Computer Science at the University of Wisconsin-Madison. I am advised by Prof. Kangwook Lee. My current research interests include both large language models (LLMs) and Multimodal Large Language Models (MLLMs), with a particular focus on Parameter-Efficient Fine-Tuning (PEFT) and In-Context Learning (ICL). I received my Master's degree in Statistics from UW-Madison in 2020, where I was advised by Prof. Miaoyan Wang. Before joining UW-Madison, I completed my Bachelor's degree in Statistics from the School of Mathematical Sciences at Nankai University in 2019.

Publications

Deep Learning with Foundation Models

Humor-Aware AI: Evaluating and Improving LLMs in Meme Analysis
Yuchen Zeng, Hemang Rajvanshy, Wonjun Kang, Jifan Zhang, Bob Mankoff, Kangwook Lee, Yixin Nie, Yipin Zhou
Under Review
[Code]

DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
Tong Xie*, Yuwei Wan*, Yixuan Liu, Yuchen Zeng, Wenjie Zhang, Chunyu Kit, Dongzhan Zhou, Bram Hoexter
Under Review
[Paper] [Code]

TabFlex: Scaling Tabular Learning to Millions with Linear Attention
Yuchen Zeng, Wonjun Kang, Andreas Mueller
Neural Information Processing Systems 2024 (NeurIPS) TRL Workshop
[Paper] [Code]

Parameter-Efficient Fine-Tuning of State Space Models
Kevin Galim*, Wonjun Kang*, Yuchen Zeng*, Hyung Il Koo, Kangwook Lee
Neural Information Processing Systems 2024 (NeurIPS) FITML Workshop
[Paper] [Code] [Summary]

Can MLLMs Perform Text-to-Image In-Context Learning?
Yuchen Zeng*, Wonjun Kang*, Yicong Chen, Hyung Il Koo, Kangwook Lee
2024 Conference on Language Modeling (COLM)
[Paper] [Code] [Summary] [45-Minute Talk]

The Expressive Power of Low-Rank Adaptation
Yuchen Zeng, Kangwook Lee
2024 International Conference on Learning Representation (ICLR)
[Paper] [Code] [Summary] [45-Minute Talk in Chinese]

Coded Prompts for Large Language Models
Ziqian Lin, Bryce Chen, Yuchen Zeng, Kangwook Lee
Neural Information Processing Systems 2023 (NeurIPS) R0-FoMo Workshop
[Paper] [Code]

LIFT: Language-Interfaced FineTuning for Non-Language Machine Learning Tasks
Tuan Dinh*, Yuchen Zeng*, Ruisu Zhang, Ziqian Lin, Michael Gira, Shashank Rajput, Jy-yong Sohn, Dimitris Papailiopoulos, Kangwook Lee
Neural Information Processing Systems 2022 (NeurIPS)
[Paper] [Code] [Summary] [5-Minute Video] [Website] [45-Minute Talk in Chinese]

Machine Learning Fairness

Federated Learning with Local Fairness Constraints
Yuchen Zeng, Hongxu Chen, Kangwook Lee
2023 IEEE International Symposium on Information Theory (ISIT)
[Paper]

Equal Improvability: A New Fairness Notion Considering the Long-Term Impact
Ozgur Guldogan*, Yuchen Zeng*, Jy-yong Sohn, Ramtin Pedarsani, Kangwook Lee
2023 International Conference on Learning Representation (ICLR)
[Paper] [Code] [Summary] [5-Minute Video] [Article]

Outlier-Robust Group Inference via Gradient Space Clustering
Yuchen Zeng, Kristjan Greenewald, Kangwook Lee, Justin Solomon, Mikhail Yurochkin
Preprint
[Paper] [Code]

Improving fairness via federated learning
Yuchen Zeng, Hongxu Chen, Kangwook Lee
Preprint
[Paper] [Code] [Summary]

Tensor Learning

Multiway clustering via tensor block models
Miaoyan Wang, Yuchen Zeng
Neural Information Processing Systems 2019 (NeurIPS)
[Paper] [Code] [3-Minute Video]

Services and Leadership

Recipient of 2024 COLM DEI Travel Scholarship
Selected to participate in Future Leaders Summit
Organizer of MLOPT Idea Seminar
Conference reviewer: NeurIPS, ICML, ICLR, TMLR, AISTATS

Work Experience

Meta, Fall 2024
Mentored by Dr. Yipin Zhou and Dr. Yixin Nie

Microsoft, Summer 2024
Mentored by Dr. Andreas Christian Müller

MIT-IBM Watson AI Lab, Summer 2022
Mentored by Dr. Mikhail Yurochkin and Prof. Justin Solomon