Papers Additive models
“Additive models” 태그가 달린 논문 252편 · 필터 해제
Automated Demand Forecasting in small to medium-sized enterprises
In response to the growing demand for accurate demand forecasts, this research proposes a generalized automated sales forecasting pipeline tailored for small- to medium-sized enterprises (SMEs). Unlike large corporations…
Additive modelsDemand ForecastingInterpretable Generalized Additive Models for Datasets with Missing Values
Many important datasets contain samples that are missing one or more feature values. Maintaining the interpretability of machine learning models in the presence of such missing data is challenging. Singly or multiply imp…
Additive modelsImputationMissing ValuesMeta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks
This paper proposes a new way to learn Physics-Informed Neural Network loss functions using Generalized Additive Models. We apply our method by meta-learning parametric partial differential equations, PDEs, on Burger's a…
Additive modelsMeta-LearningDNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive Models
Survival analysis is a classic problem in statistics with important applications in healthcare. Most machine learning models for survival analysis are black-box models, limiting their use in healthcare settings where int…
Additive modelsSurvival AnalysisProgression: an extrapolation principle for regression
The problem of regression extrapolation, or out-of-distribution generalization, arises when predictions are required at test points outside the range of the training data. In such cases, the non-parametric guarantees for…
Additive modelsOut-of-Distribution GeneralizationregressionInherently Interpretable Tree Ensemble Learning
Tree ensemble models like random forests and gradient boosting machines are widely used in machine learning due to their excellent predictive performance. However, a high-performance ensemble consisting of a large number…
Additive modelsEnsemble LearningTransparent Networks for Multivariate Time Series
Transparent models, which are machine learning models that produce inherently interpretable predictions, are receiving significant attention in high-stakes domains. However, despite much real-world data being collected a…
Additive modelsTime SeriesGeneralized Sparse Additive Model with Unknown Link Function
Generalized additive models (GAM) have been successfully applied to high dimensional data analysis. However, most existing methods cannot simultaneously estimate the link function, the component functions and the variabl…
Additive modelsBilevel OptimizationVariable SelectionProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learning
Generalized additive models (GAMs) have long been a powerful white-box tool for the intelligible analysis of tabular data, revealing the influence of each feature on the model predictions. Despite the success of neural n…
Additive modelsA Functional Extension of Semi-Structured Networks
Semi-structured networks (SSNs) merge the structures familiar from additive models with deep neural networks, allowing the modeling of interpretable partial feature effects while capturing higher-order non-linearities at…
Additive modelsGAMformer: In-Context Learning for Generalized Additive Models
Generalized Additive Models (GAMs) are widely recognized for their ability to create fully interpretable machine learning models for tabular data. Traditionally, training GAMs involves iterative learning algorithms, such…
Additive modelsIn-Context LearningInterpretable Machine LearningQuantifying Visual Properties of GAM Shape Plots: Impact on Perceived Cognitive Load and Interpretability
Generalized Additive Models (GAMs) offer a balance between performance and interpretability in machine learning. The interpretability aspect of GAMs is expressed through shape plots, representing the model's decision-mak…
Additive modelsDecision MakingChallenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models
Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance advantages, whereas interpretable models a…
Additive modelsInterpretable Machine LearningSpeech as a Biomarker for Disease Detection
Speech is a rich biomarker that encodes substantial information about the health of a speaker, and thus it has been proposed for the detection of numerous diseases, achieving promising results. However, questions remain …
Additive modelsAssociations between exposure to OPEs and rheumatoid arthritis risk among adults in NHANES, 2011-2018
Rheumatoid arthritis (RA) has an intricate etiology that includes environmental factors as well as genetics. Organophosphate esters (OPEs) are frequently used as chemical additives in many personal care products and hous…
Additive modelsNutritionA novel method to separate circadian from non-circadian masking effects in order to enhance daily circadian timing and amplitude estimation from core body temperature
Circadian disruption contributes to adverse effects on sleep, performance, and health. One accepted method to track continuous daily changes in circadian timing is to measure core body temperature (CBT), and establish da…
Additive modelsNeurCAM: Interpretable Neural Clustering via Additive Models
Interpretable clustering algorithms aim to group similar data points while explaining the obtained groups to support knowledge discovery and pattern recognition tasks. While most approaches to interpretable clustering co…
Additive modelsClusteringText ClusteringneuralGAM: Explainable generalized additive neural networks with independent neural network training
Neural Networks are one of the most popular methods nowadays given their high performance on diverse tasks, such as computer vision, anomaly detection, computer-aided disease detection and diagnosis, or natural language …
Additive modelsAnomaly DetectionDecision MakingCombinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products
To facilitate effective decision-making, precipitation datasets should include uncertainty estimates. Quantile regression with machine learning has been proposed for issuing such estimates. Distributional regression offe…
Additive modelsEnsemble Learningquantile regressionregression+1CAT: Interpretable Concept-based Taylor Additive Models
As an emerging interpretable technique, Generalized Additive Models (GAMs) adopt neural networks to individually learn non-linear functions for each feature, which are then combined through a linear model for final predi…
Additive models