4 papers
Estimating Implicit Regularization in Deep Learning
Joseph H. Rudoler, Kevin Tan, Giles Hooker +1
Deep learning systems are known to exhibit implicit regularization (alt. implicit bias), favoring simple solutions instead of merely minimizing the loss function. In some cases, we…
Statistical Inference for Explainable Boosting Machines
Haimo Fang, Kevin Tan, Jonathan Pipping-Gamon +1
Explainable boosting machines (EBMs) are popular "glass-box" models that learn a set of univariate functions using boosting trees. These achieve explainability through visualizatio…
Statistical Inference for Gradient Boosting Regression
Haimo Fang, Kevin Tan, Giles Hooker
Gradient boosting is widely popular due to its flexibility and predictive accuracy. However, statistical inference and uncertainty quantification for gradient boosting remain chall…
Accelerated Inference for Partially Observed Markov Processes using Automatic Differentiation
Kevin Tan, Giles Hooker, Edward L. Ionides
Automatic differentiation (AD) has driven recent advances in machine learning, including deep neural networks and Hamiltonian Markov Chain Monte Carlo methods. Partially observed n…