Yawei Li

About

I am a Ph.D. student focused on Trustworthy Deep Learning, with a particular emphasis on probabilistic methods and explainability, and their scalability to large language models. For more information—including publications, news, projects, and my CV—please visit my personal website. If you are interested in research collaboration or master thesis supervision, feel free to contact me (my email address is available on the website).

You can find me on

Selected publications

  1. Li Y, Rügamer D, Bischl B, Rezaei M (2025) Calibrating LLMs with Information-Theoretic Evidential Deep Learning The Thirteenth International Conference on Learning Representations (ICLR),
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  2. Li* Y, Zhang* Y, Kawaguchi K, Khakzar A, Bischl B, Rezaei M (2024) A Dual-Perspective Approach to Evaluating Feature Attribution Methods. Transactions on Machine Learning Research (TMLR).
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  3. Zhang* Y, Li* Y, Wang X, Shen Q, Plank B, Bischl B, Rezaei M, Kawaguchi K (2024) FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models. Compression Workshop at NeurIPS 2024.
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  4. Vahidi A, Schoßer S, Wimmer L, Li Y, Bischl B, Hüllermeier E, Rezaei M (2024) Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization The Twelfth International Conference on Learning Representations (ICLR),
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  5. Zhang* Y, Li* Y, Brown H, Rezaei M, Bischl B, Torr P, Khakzar A, Kawaguchi K (2023) AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments. NeurIPS 2023 Workshop XAI in Action.
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