Additive models
71 papers with code • 0 benchmarks • 0 datasets
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Fast Interpretable Greedy-Tree Sums
In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure.
Fast Sparse Classification for Generalized Linear and Additive Models
For fast sparse logistic regression, our computational speed-up over other best-subset search techniques owes to linear and quadratic surrogate cuts for the logistic loss that allow us to efficiently screen features for elimination, as well as use of a priority queue that favors a more uniform exploration of features.
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models.
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions--potentially causing harms once deployed.
Additive Gaussian Processes
We introduce a Gaussian process model of functions which are additive.
Sparse Partially Linear Additive Models
Thus, to make a GPLAM a viable approach in situations in which little is known $a~priori$ about the features, one must overcome two primary model selection challenges: deciding which features to include in the model and determining which of these features to treat nonlinearly.
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases
To this end, we build a surrogate function to approximate the target distribution using properly chosen random bases and an efficient optimization process.
Chained Gaussian Processes
Gaussian process models are flexible, Bayesian non-parametric approaches to regression.
Stability selection for component-wise gradient boosting in multiple dimensions
We apply this new algorithm to a study to estimate abundance of common eider in Massachusetts, USA, featuring excess zeros, overdispersion, non-linearity and spatio-temporal structures.
Sparse hierarchical interaction learning with epigraphical projection
This work focuses on learning optimization problems with quadratical interactions between variables, which go beyond the additive models of traditional linear learning.
