Learning Explainable Models Using Attribution Priors. - Gabriel G. Erion; Joseph D. Janizek; Pascal Sturmfels; Scott M. Lundberg; Su-In Lee (CoRR, 2019)

Learned Feature Attribution Priors. - Ethan Weinberger; Joseph D. Janizek; Su-In Lee (CoRR, 2019)

An adversarial approach for the robust classification of pneumonia from chest radiographs. - Joseph D. Janizek; Gabriel G. Erion; Alex J. DeGrave; Su-In Lee (CHIL, 2020)

Learning Deep Attribution Priors Based On Prior Knowledge. - Ethan Weinberger; Joseph D. Janizek; Su-In Lee (NeurIPS, 2020)

Adversarial deconfounding autoencoder for learning robust gene expression embeddings. - Ayse Berceste Dincer; Joseph D. Janizek; Su-In Lee (Bioinform., 2020)

True to the Model or True to the Data? - Hugh Chen; Joseph D. Janizek; Scott M. Lundberg; Su-In Lee (CoRR, 2020)

Explaining Explanations: Axiomatic Feature Interactions for Deep Networks. - Joseph D. Janizek; Pascal Sturmfels; Su-In Lee (J. Mach. Learn. Res., 2021)

AI for radiographic COVID-19 detection selects shortcuts over signal. - Alex J. DeGrave; Joseph D. Janizek; Su-In Lee (Nat. Mach. Intell., 2021)

Improving performance of deep learning models with axiomatic attribution priors and expected gradients. - Gabriel G. Erion; Joseph D. Janizek; Pascal Sturmfels; Scott M. Lundberg; Su-In Lee (Nat. Mach. Intell., 2021)

Understanding Biomedical Machine Learning Models - Joseph D. Janizek (2022)

LAB-Bench: Measuring Capabilities of Language Models for Biology Research. - Jon M. Laurent; Joseph D. Janizek; Michael Ruzo; Michaela M. Hinks; Michael J. Hammerling; Siddharth Narayanan; Manvitha Ponnapati; Andrew D. White; Samuel G. Rodriques (CoRR, 2024)

BiasICL: In-Context Learning and Demographic Biases of Vision Language Models. - Sonnet Xu; Joseph D. Janizek; Yixing Jiang; Roxana Daneshjou (CoRR, 2025)