1.4k citations · 3.1k across the 30 of their papers we have counts for
31 papers
Transcriptome-informed multi-modal AI for predicting neoadjuvant therapy response from breast cancer biopsies
Jungkyu Park, Dhruva Biswas, Joseph Cappadona +22
Scarcity of labeled data limits development of deep learning biomarkers in oncology. We develop a two-stage AI model predicting pathological complete response (pCR) to neoadjuvant…
Extending Explainable Boosting Machines to Scientific Image Data
Daniel Schug, Sai Yerramreddy, Rich Caruana +2
As the deployment of computer vision technology becomes increasingly common in science, the need for explanations of the system and its output has become a focus of great concern.…
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse
Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana +1
Machine learning (ML) recourse techniques are increasingly used in high-stakes domains, providing end users with actions to alter ML predictions, but they assume ML developers unde…
Augmenting Interpretable Models with LLMs during Training
Chandan Singh, Armin Askari, Rich Caruana +1
Recent large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks. However, their proliferation into high-stakes domains (e.g. me…
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Zijie J. Wang, Alex Kale, Harsha Nori +6
Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions--potentially causing harms once deployed. However,…
Differentially Private Estimation of Heterogeneous Causal Effects
Fengshi Niu, Harsha Nori, Brian Quistorff +3
Estimating heterogeneous treatment effects in domains such as healthcare or social science often involves sensitive data where protecting privacy is important. We introduce a gener…