Che-Ping Tsai

Che-Ping Tsai

Ph.D. student
at Carnegie Mellon University

Bio

I’m a fifth year PhD student in the Machine Learning Department at Carnegie Mellon University advised by Pradeep Ravikumar. My research centers around statistical machine learning, with a focus on developing principal algorithms for interpretability and Self-Supervised Learning. I interned at Amazon as an applied scientist, where I worked on large language models for tabular data and recommender systems.

Previously, I completed my M.S. degree in Computer Science & Information Engineering (CSIE) at National Taiwan University (NTU). I was a member of the Speech Processing Lab working with Lin-shan Lee and Prof. Hung-yi Lee, working on speech recognition and multi-label classification. During my undergrad years, I pursued a dual major in Electrical Engineering and Mathematics at NTU.

Publications

  • AnoLLM: Large Language Models for Tabular Anomaly Detection
    Che-Ping Tsai, Phil Wallis, Ganyu Teng, Wei Ding.
    In International Conference on Learning Representations (ICLR), 2025.
    [paper | [code]]

  • Sample based Explanations via Generalized Representers
    Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar.
    In Advances in Neural Information Processing Systems (NeurIPS), 2023.
    [paper]

  • Representer Point Selection for Explaining Regularized High-dimensional Models
    Che-Ping Tsai, Jiong Zhang, Hsiang-Fu Yu, Eli Chien, Cho-Jui Hsieh, Pradeep Ravikumar.
    In International Conference on Machine Learning (ICML), 2023
    [paper | code]

  • Faith-Shap: The Faithful Shapley Interaction Index
    Che-Ping. Tsai, Chih-Kuan. Yeh, Pradeep Ravikumar.
    Journal of Machine Learning Research (JMLR), Vol. 24 (94), pages 1-42, 2023.
    [paper | code]

  • Heavy-tailed streaming statistical estimation
    Che-Ping. Tsai,A. Prasad, S. Balakrishnan, P. Ravikumar.
    In International Conference on Artificial Intelligence and Statistics (AISTATS) 25, 2022 (Oral).
    [paper]

  • Order-free Learning Alleviating Exposure Bias in Multi-label Classification
    Che-Ping Tsai, Hung-Yi Lee.
    In AAAI Conference on Artificial Intelligence (AAAI), 2020.
    [paper| code]]

  • Completely Unsupervised Phoneme Recognition By A Generative Adversarial Network Harmonized with Iteratively Refined Hidden Markov Models
    Kuan-Yu Chen, Che-Ping Tsai, Da-Rong Liu, Hung-Yi Lee, Lin-shan Lee.
    In Interspeech, 2019.
    [paper | code]

  • Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification
    Che-Ping Tsai, Hung-Yi Lee.
    In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.
    [paper | code]

  • Transcribing lyrics from commercial song audio: the first step towards singing content processing
    Che-Ping Tsai*, Yi-Lin Tuan, Hung-Yi Lee, Lin-shan Lee.
    *In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018.
    [paper | code]

Work experience

Awards

  • Silver Medal, 53nd International Mathematical Olympiad(IMO), Mar del Plata, Argentina, 2012.

  • Silver Medal, 53nd International Mathematical Olympiad(IMO), Amsterdam, Netherland, 2011.

Services