1.1k citations · 5.4k across the 59 of their papers we have counts for
59 papers
A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds
Yan Shuo Tan, Abhineet Agarwal, Bin Yu
Decision trees are important both as interpretable models amenable to high-stakes decision-making, and as building blocks of ensemble methods such as random forests and gradient bo…
Towards Robust Waveform-Based Acoustic Models
Dino Oglic, Zoran Cvetkovic, Peter Sollich +2
We study the problem of learning robust acoustic models in adverse environments, characterized by a significant mismatch between training and test conditions. This problem is of pa…
Interpreting and improving deep-learning models with reality checks
Chandan Singh, Wooseok Ha, Bin Yu
Recent deep-learning models have achieved impressive predictive performance by learning complex functions of many variables, often at the cost of interpretability. This chapter cov…
Seven Principles for Rapid-Response Data Science: Lessons Learned from Covid-19 Forecasting
Bin Yu, Chandan Singh
In this article, we take a step back to distill seven principles out of our experience in the spring of 2020, when our 12-person rapid-response team used skills of data science and…
A stability-driven protocol for drug response interpretable prediction (staDRIP)
Xiao Li, Tiffany M. Tang, Xuewei Wang +2
Modern cancer -omics and pharmacological data hold great promise in precision cancer medicine for developing individualized patient treatments. However, high heterogeneity and nois…
Stable discovery of interpretable subgroups via calibration in causal studies
Raaz Dwivedi, Yan Shuo Tan, Briton Park +4
Building on Yu and Kumbier's PCS framework and for randomized experiments, we introduce a novel methodology for Stable Discovery of Interpretable Subgroups via Calibration (StaDISC…