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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…
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…
Transformation Importance with Applications to Cosmology
Chandan Singh, Wooseok Ha, Francois Lanusse +3
Machine learning lies at the heart of new possibilities for scientific discovery, knowledge generation, and artificial intelligence. Its potential benefits to these fields requires…
A Debiased MDI Feature Importance Measure for Random Forests
Xiao Li, Yu Wang, Sumanta Basu +2
Tree ensembles such as Random Forests have achieved impressive empirical success across a wide variety of applications. To understand how these models make predictions, people rout…
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees
Summer Devlin, Chandan Singh, W. James Murdoch +1
Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine learning models known for achieving strong predictive performance across a wide variety of domains. Howe…
Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients
Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright +1
Hamiltonian Monte Carlo (HMC) is a state-of-the-art Markov chain Monte Carlo sampling algorithm for drawing samples from smooth probability densities over continuous spaces. We stu…