Xingchen Wan

Research Scientist

Google

About me

I am a Research Scientist at Google based in the San Francisco Bay Area.

I did my DPhil (the Oxford way of saying PhD) in the Machine Learning Research Group, Department of Engineering Science, University of Oxford. I was also a Clarendon Scholar and a member of St John’s College, both at the University of Oxford. I previously interned at Google and Meta.

Academic Services

Reviewer/program committee member at ACL (2023-24), AutoML-Conf (2023-24), COLM (2024), CVPR (2024), ECCV (2024), EMNLP (2023-24), ICLR (2024-25), ICML (2023-24), JMLR, Machine Learning, NeurIPS (2022-23), WACV (2022-24), etc.

Area chair at NeurIPS (2024); Action editor at TMLR.

Interests

Large language models
Bayesian optimization
Automated machine learning (AutoML)
Machine learning on graphs

Education

DPhil (PhD), Machine Learning, 2019 - 2023

University of Oxford
BA, MEng, Engineering Science. First-class Honours, 2015 - 2019

University of Oxford

Featured

Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization

Comprehensive study powering the Google Cloud Vertex AI Prompt Optimizer (NeurIPS 2024).

Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering

Mitigating prompt biases and unifying existing calibration approaches without labeled data (ICLR 2024)

AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning

Automatic discovery of families of high-performing PEFT configurations (TACL 2024)

Introducing Self-Adaptive Prompting for Large Language Models

Adaptive, black-box and automatic prompting methods to improve LLMs in zero-shot settings (ACL & EMNLP 2023)

Publications

View all listed publications or view by tags. For a complete list including preprints & working papers, refer to my Google Scholar.

Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Sercan Ö. Arık (2024). Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization. Advances in Neural Information Processing Systems (NeurIPS).

PDF Cite Abstract OpenReview

Hanjun Dai, Bethany Yixin Wang, Xingchen Wan, Bo Dai, Sherry Yang, Azade Nova, Pengcheng Yin, Phitchaya Mangpo Phothilimthana, Charles Sutton, Dale Schuurmans (2024). UQE: A Query Engine for Unstructured Databases. Advances in Neural Information Processing Systems (NeurIPS).

PDF Cite Abstract OpenReview

Huidong Liang, Xingchen Wan, Xiaowen Dong (2024). Bayesian Optimization of Functions over Node Subsets in Graphs. Advances in Neural Information Processing Systems (NeurIPS).

PDF Cite Code Abstract OpenReview

Han Zhou, Xingchen Wan, Yinhong Liu, Nigel Collier, Ivan Vulić, Anna Korhonen (2024). Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments. Empirical Methods in Natural Language Processing (EMNLP).

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Han Zhou, Xingchen Wan, Lev Proleev, Diana Mincu, Jilin Chen, Katherine Heller, Subhrajit Roy (2024). Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering. International Conference on Learning Representations (ICLR).

PDF Cite Google Research Blog Abstract OpenReview

Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Xingchen Wan, Vu Nguyen, Harald Oberhauser, Michael Osborne (2024). Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach. International Conference on Artificial Intelligence and Statistics (AISTATS).

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Dongyu Gong, Xingchen Wan, Dingmin Wang (2024). Working Memory Capacity of ChatGPT: An Empirical Study. AAAI Conference on Artificial Intelligence (AAAI).

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Han Zhou, Xingchen Wan, Ivan Vulić, Anna Korhonen (2024). AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning. Transactions of the Association for Computational Linguistics (TACL).

PDF Cite Code DOI Abstract

Diego Granziol, Nick P. Baskerville, Xingchen Wan, Samuel Albanie, Stephen Roberts (2024). Iterate Averaging in the Quest for Best Test Error. Journal of Machine Learning Research (JMLR).

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Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan Ö. Arık, Tomas Pfister (2023). Universal Self-Adaptive Prompting. Empirical Methods in Natural Language Processing (EMNLP).

PDF Cite Google Research Blog Abstract (Google Research) OpenReview ACL Anthology Slides & Talk

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