Papers Additive models
“Additive models” 태그가 달린 논문 252편 · 필터 해제
Identification with possibly invalid IVs
This paper proposes a novel identification strategy relying on quasi-instrumental variables (quasi-IVs). A quasi-IV is a relevant but possibly invalid IV because it is not exogenous or not excluded. We show that a variet…
Additive modelsvalidIdentification of Additive Continuous-time Systems in Open and Closed loop
When identifying electrical, mechanical, or biological systems, parametric continuous-time identification methods can lead to interpretable and parsimonious models when the model structure aligns with the physical proper…
Additive modelsStochastic Gradient Descent for Nonparametric Regression
This paper introduces an iterative algorithm for training nonparametric additive models that enjoys favorable memory storage and computational requirements. The algorithm can be viewed as the functional counterpart of st…
Additive modelsregressionEnhancing CVaR portfolio optimisation performance with GAM factor models
We propose a discrete-time econometric model that combines autoregressive filters with factor regressions to predict stock returns for portfolio optimisation purposes. In particular, we test both robust linear regression…
Additive modelsModeling non-linear Effects with Neural Networks in Relational Event Models
Dynamic networks offer an insight of how relational systems evolve. However, modeling these networks efficiently remains a challenge, primarily due to computational constraints, especially as the number of observed event…
Additive modelsComputational EfficiencyManagementGeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Models
This paper introduces GeoShapley, a game theory approach to measuring spatial effects in machine learning models. GeoShapley extends the Nobel Prize-winning Shapley value framework in game theory by conceptualizing locat…
Additive modelsFocusLearn: Fully-Interpretable, High-Performance Modular Neural Networks for Time Series
Multivariate time series have many applications, from healthcare and meteorology to life science. Although deep learning models have shown excellent predictive performance for time series, they have been criticised for b…
Additive modelsfeature selectionTime SeriesTime Series Forecasting+1Handling Overlapping Asymmetric Datasets -- A Twice Penalized P-Spline Approach
Overlapping asymmetric datasets are common in data science and pose questions of how they can be incorporated together into a predictive analysis. In healthcare datasets there is often a small amount of information that …
Additive modelsDimensionality ReductionImputationHow Well Do Feature-Additive Explainers Explain Feature-Additive Predictors?
Surging interest in deep learning from high-stakes domains has precipitated concern over the inscrutable nature of black box neural networks. Explainable AI (XAI) research has led to an abundance of explanation algorithm…
Additive modelsAttributeDecision MakingContext-aware feature attribution through argumentation
Feature attribution is a fundamental task in both machine learning and data analysis, which involves determining the contribution of individual features or variables to a model's output. This process helps identify the m…
Additive modelsInfluenza Hospitalisations in England during the 2022/23 Season: do different data sources drive divergence in modelled waves? A comparison of surveillance and administrative data
Accurate and representative data is vital for precisely reporting the impact of influenza in healthcare systems. Northern hemisphere winter 2022/23 experienced the most substantial influenza wave since the COVID-19 pande…
Additive modelsTheoretical Evaluation of Asymmetric Shapley Values for Root-Cause Analysis
In this work, we examine Asymmetric Shapley Values (ASV), a variant of the popular SHAP additive local explanation method. ASV proposes a way to improve model explanations incorporating known causal relations between var…
Additive modelsDiscrete-Choice Model with Generalized Additive Utility Network
Discrete-choice models are a powerful framework for analyzing decision-making behavior to provide valuable insights for policymakers and businesses. Multinomial logit models (MNLs) with linear utility functions have been…
Additive modelsDecision MakingDiscrete Choice ModelsmodelPseudo Label Selection is a Decision Problem
Pseudo-Labeling is a simple and effective approach to semi-supervised learning. It requires criteria that guide the selection of pseudo-labeled data. The latter have been shown to crucially affect pseudo-labeling's gener…
Additive modelsModel SelectionPseudo LabelRegionally Additive Models: Explainable-by-design models minimizing feature interactions
Generalized Additive Models (GAMs) are widely used explainable-by-design models in various applications. GAMs assume that the output can be represented as a sum of univariate functions, referred to as components. However…
Additive modelsCan local explanation techniques explain linear additive models?
Local model-agnostic additive explanation techniques decompose the predicted output of a black-box model into additive feature importance scores. Questions have been raised about the accuracy of the produced local additi…
Additive modelsExplanation Fidelity EvaluationFeature ImportanceLLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs
We show that large language models (LLMs) are remarkably good at working with interpretable models that decompose complex outcomes into univariate graph-represented components. By adopting a hierarchical approach to reas…
Additive modelsImproving Neural Additive Models with Bayesian Principles
Neural additive models (NAMs) enhance the transparency of deep neural networks by handling input features in separate additive sub-networks. However, they lack inherent mechanisms that provide calibrated uncertainties an…
Additive modelsBayesian Inferencetabular-regressionCurve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
Generalized Additive Models (GAMs) have recently experienced a resurgence in popularity due to their interpretability, which arises from expressing the target value as a sum of non-linear transformations of the features.…
Additive modelsTime SeriesRepresenting Additive Gaussian Processes by Sparse Matrices
Among generalized additive models, additive Mat\'ern Gaussian Processes (GPs) are one of the most popular for scalable high-dimensional problems. Thanks to their additive structure and stochastic differential equation re…
Additive modelsBayesian OptimizationGaussian Processes