Papers Additive models
“Additive models” 태그가 달린 논문 252편 · 필터 해제
How to address monotonicity for model risk management?
In this paper, we study the problem of establishing the accountability and fairness of transparent machine learning models through monotonicity. Although there have been numerous studies on individual monotonicity, pairw…
Additive modelsFairnessManagementThe Structurally Complex with Additive Parent Causality (SCARY) Dataset
Causal datasets play a critical role in advancing the field of causality. However, existing datasets often lack the complexity of real-world issues such as selection bias, unfaithful data, and confounding. To address thi…
Additive modelsCausal DiscoverySelection biasMinimax Signal Detection in Sparse Additive Models
Sparse additive models are an attractive choice in circumstances calling for modelling flexibility in the face of high dimensionality. We study the signal detection problem and establish the minimax separation rate for t…
Additive modelsCausal Discovery with Score Matching on Additive Models with Arbitrary Noise
Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability. Moreover additional restrictions are often imposed in order to simplify the inference task: thi…
Additive modelsCausal DiscoveryExploring and Interacting with the Set of Good Sparse Generalized Additive Models
In real applications, interaction between machine learning models and domain experts is critical; however, the classical machine learning paradigm that usually produces only a single model does not facilitate such intera…
Additive modelsInterpretable Reinforcement Learning via Neural Additive Models for Inventory Management
The COVID-19 pandemic has highlighted the importance of supply chains and the role of digital management to react to dynamic changes in the environment. In this work, we focus on developing dynamic inventory ordering pol…
Additive modelsManagementreinforcement-learningReinforcement Learning+1GAM Coach: Towards Interactive and User-centered Algorithmic Recourse
Machine learning (ML) recourse techniques are increasingly used in high-stakes domains, providing end users with actions to alter ML predictions, but they assume ML developers understand what input variables can be chang…
Additive modelscounterfactualStructural Neural Additive Models: Enhanced Interpretable Machine Learning
Deep neural networks (DNNs) have shown exceptional performances in a wide range of tasks and have become the go-to method for problems requiring high-level predictive power. There has been extensive research on how DNNs …
Additive modelsInterpretable Machine LearningApproximately Bayes-Optimal Pseudo Label Selection
Semi-supervised learning by self-training heavily relies on pseudo-label selection (PLS). The selection often depends on the initial model fit on labeled data. Early overfitting might thus be propagated to the final mode…
Additive modelsPseudo LabelNowcasting the 2022 mpox outbreak in England
In May 2022, a cluster of mpox cases were detected in the UK that could not be traced to recent travel history from an endemic region. Over the coming months, the outbreak grew, with over 3000 total cases reported in the…
Additive modelsFrugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models
We focus on day-ahead electricity load forecasting of substations of the distribution network in France; therefore, our problem lies between the instability of a single consumption and the stability of a countrywide tota…
Additive modelsLoad ForecastingTime SeriesTime Series Analysis+1Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this success, the inner workings of DNNs are …
Additive modelsMonotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance
Algorithm fairness in the application of artificial intelligence (AI) is essential for a better society. As the foundational axiom of social mechanisms, fairness consists of multiple facets. Although the machine learning…
Additive modelsEthicsExplainable Artificial Intelligence (XAI)FairnessExtractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection
Automatic Text Summarization (ATS) is becoming relevant with the growth of textual data; however, with the popularization of public large-scale datasets, some recent machine learning approaches have focused on dense mode…
Additive modelsBinary ClassificationExtractive SummarizationExtractive Text Summarization+2Classification by sparse generalized additive models
We consider (nonparametric) sparse (generalized) additive models (SpAM) for classification. The design of a SpAM classifier is based on minimizing the logistic loss with a sparse group Lasso/Slope-type penalties on the c…
Additive modelsClassificationEnsemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification
We propose a novel, succinct, and effective approach for distribution prediction to quantify uncertainty in machine learning. It incorporates adaptively flexible distribution prediction of $\mathbb{P}(\mathbf{y}|\mathbf{…
Additive modelsregressionUncertainty QuantificationEstimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML
Treatment protocols, disease understanding, and viral characteristics changed over the course of the COVID-19 pandemic; as a result, the risks associated with patient comorbidities and biomarkers also changed. We add to …
Additive modelsAdditive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great Britain
Forecasts of regional electricity net-demand, consumption minus embedded generation, are an essential input for reliable and economic power system operation, and energy trading. While such forecasts are typically perform…
Additive modelsenergy tradingModel SelectionRethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning
We introduce a family of interpretable machine learning models, with two broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous logistic link function in General Additive Models (GAMs); and Subsc…
Additive modelsBinary ClassificationInterpretable Machine LearningIndividualized and Global Feature Attributions for Gradient Boosted Trees in the Presence of $\ell_2$ Regularization
While $\ell_2$ regularization is widely used in training gradient boosted trees, popular individualized feature attribution methods for trees such as Saabas and TreeSHAP overlook the training procedure. We propose Predic…
Additive modelsfeature selection