Yawei Li

Affiliation. Email.

yawei_photo.JPG

Institut für Statistik

LMU München

Ludwigstraße 33, 80539

München, Germany

My academic journey started in China, where I obtained a bachelor in Laboratory Medical Science from Southern Medical University in Guangzhou, China. However, my passion for engineering led me to pursue a B.Sc. in Electrical Engineering at TU Darmstadt in Germany. Intrigued by the intersection of technology and medicine, I then completed a M.Sc. in Biomedical Computing at Technical University of Munich (TUM).

Currently, I am a Ph.D. student at the Statistical Learning and Data Science (SLDS) group at LMU Munich, under the supervision of Prof. Dr. Bernd Bischl. I am also a junior member of Munich Center for Machine Learning (MCML). In 2024, I was also a visiting researcher at the Deep Learning Lab of Prof. Dr. Kenji Kawaguchi at the National University of Singapore (NUS).

My research interests lie in trustworthy deep learning, with a particular focus on explainability and uncertainty quantification.

news

Jun 15, 2025 I started my internship as Applied Scientist Intern at Amazon.
Jan 22, 2025 Our paper Calibrating LLMs with Information-Theoretic Evidential Deep Learning is accepted by ICLR 2025.
Nov 01, 2024 Our paper A Dual-Perspective Approach to Evaluating Feature Attribution Methods is accepted by Transactions on Machine Learning Research (TMLR).
Oct 09, 2024 Our paper FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models is accepted by Compression Workshop at NeurIPS 2024.
Feb 09, 2024 The paper Probabilistic Self-supervised Learning via Scoring Rules Minimization is accepted by ICLR 2024.

selected publications

  1. Calibrating LLMs with Information-Theoretic Evidential Deep Learning
    Yawei Li, David Rügamer, Bernd Bischl, and 1 more author
    In The Thirteenth International Conference on Learning Representations (ICLR), 2025
    PDF
  2. A Dual-Perspective Approach to Evaluating Feature Attribution Methods
    Yawei Li*, Yang Zhang*, Kenji Kawaguchi, and 3 more authors
    Transactions on Machine Learning Research (TMLR), 2024
    PDF
  3. FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models
    Yang Zhang*, Yawei Li*, Xinpeng Wang, and 5 more authors
    Compression Workshop at NeurIPS 2024, 2024
    PDF
  4. Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization
    Amirhossein Vahidi, Simon Schoßer, Lisa Wimmer, and 4 more authors
    In The Twelfth International Conference on Learning Representations (ICLR), 2024
    PDF
  5. AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments
    Yang Zhang*, Yawei Li*, Hannah Brown, and 5 more authors
    NeurIPS 2023 Workshop XAI in Action, 2023
    PDF
  6. Deep learning-based classification of dermatological lesions given a limited amount of labelled data
    S Krammer, Y Li, N Jakob, and 6 more authors
    Journal of the European Academy of Dermatology and Venereology, 2022
    PDF
  7. Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information
    Yang* Zhang, Ashkan* Khakzar, Yawei Li, and 3 more authors
    Advances in Neural Information Processing Systems (NeurIPS), 2021
    PDF
  8. Explaining covid-19 and thoracic pathology model predictions by identifying informative input features
    Ashkan* Khakzar, Yang* Zhang, Wejdene Mansour, and 5 more authors
    In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021
    PDF