Papers Additive models
“Additive models” 태그가 달린 논문 252편 · 필터 해제
Are Logistic Models Really Interpretable?
The demand for open and trustworthy AI models points towards widespread publishing of model weights. Consumers of these model weights must be able to act accordingly with the information provided. That said, one of the s…
Additive modelsTowards Bayesian Data Selection
A wide range of machine learning algorithms iteratively add data to the training sample. Examples include semi-supervised learning, active learning, multi-armed bandits, and Bayesian optimization. We embed this kind of d…
Active LearningAdditive modelsBayesian OptimizationMulti-Armed BanditsTensor Polynomial Additive Model
Additive models can be used for interpretable machine learning for their clarity and simplicity. However, In the classical models for high-order data, the vectorization operation disrupts the data structure, which may le…
Additive modelsDecision MakingInterpretable Machine LearningmodelThe Intelligible and Effective Graph Neural Additive Networks
Graph Neural Networks (GNNs) have emerged as the predominant approach for learning over graph-structured data. However, most GNNs operate as black-box models and require post-hoc explanations, which may not suffice in hi…
Additive modelsEfficient mid-term forecasting of hourly electricity load using generalized additive models
Accurate mid-term (weeks to one year) hourly electricity load forecasts are essential for strategic decision-making in power plant operation, ensuring supply security and grid stability, planning and building energy stor…
Additive modelsenergy tradingLoad ForecastingMulti-Level Additive Modeling for Structured Non-IID Federated Learning
The primary challenge in Federated Learning (FL) is to model non-IID distributions across clients, whose fine-grained structure is important to improve knowledge sharing. For example, some knowledge is globally shared ac…
Additive modelsFederated LearningConcept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images
To increase the transparency of modern computer-aided diagnosis (CAD) systems for assessing the malignancy of lung nodules, an interpretable model based on applying the generalized additive models and the concept-based l…
Additive modelsAttributeDecision MakingAttention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers
We address the critical challenge of applying feature attribution methods to the transformer architecture, which dominates current applications in natural language processing and beyond. Traditional attribution methods t…
Additive modelsFeature ImportanceExplainable Automatic Grading with Neural Additive Models
The use of automatic short answer grading (ASAG) models may help alleviate the time burden of grading while encouraging educators to frequently incorporate open-ended items in their curriculum. However, current state-of-…
Additive modelsautomatic short answer gradingFeature EngineeringShape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations
Symbolic regression has excelled in uncovering equations from physics, chemistry, biology, and related disciplines. However, its effectiveness becomes less certain when applied to experimental data lacking inherent close…
Additive modelsFormscientific discoverySymbolic RegressionHierarchical Neural Additive Models for Interpretable Demand Forecasts
Demand forecasts are the crucial basis for numerous business decisions, ranging from inventory management to strategic facility planning. While machine learning (ML) approaches offer accuracy gains, their interpretabilit…
Additive modelsManagementTime SeriesDistilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regression
Recent advancements in reinforcement learning (RL) have led to remarkable achievements in robot locomotion capabilities. However, the complexity and ``black-box'' nature of neural network-based RL policies hinder their i…
Additive modelsReinforcement Learning (RL)Symbolic RegressionOn Training Survival Models with Scoring Rules
Scoring rules are an established way of comparing predictive performances across model classes. In the context of survival analysis, they require adaptation in order to accommodate censoring. This work investigates using…
Additive modelsscoring ruleSurvival AnalysisIGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight
Feature selection is a critical component in predictive analytics that significantly affects the prediction accuracy and interpretability of models. Intrinsic methods for feature selection are built directly into model l…
Additive modelsfeature selectionNeural Additive Image Model: Interpretation through Interpolation
Understanding how images influence the world, interpreting which effects their semantics have on various quantities and exploring the reasons behind changes in image-based predictions are highly difficult yet extremely i…
Additive modelsmodelData Science with LLMs and Interpretable Models
Recent years have seen important advances in the building of interpretable models, machine learning models that are designed to be easily understood by humans. In this work, we show that large language models (LLMs) are …
Additive modelsQuestion AnsweringFAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML
We present FAST, an optimization framework for fast additive segmentation. FAST segments piecewise constant shape functions for each feature in a dataset to produce transparent additive models. The framework leverages a …
Additive modelsComputational Efficiencyfeature selectionGaussian Process Neural Additive Models
Deep neural networks have revolutionized many fields, but their black-box nature also occasionally prevents their wider adoption in fields such as healthcare and finance, where interpretable and explainable models are re…
Additive modelsExplainable ModelsUse of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data
This paper proposes two methods for causal additive models with unobserved variables (CAM-UV). CAM-UV assumes that the causal functions take the form of generalized additive models and that latent confounders are present…
Additive modelsCausal DiscoveryTime SeriesBoosting Causal Additive Models
We present a boosting-based method to learn additive Structural Equation Models (SEMs) from observational data, with a focus on the theoretical aspects of determining the causal order among variables. We introduce a fami…
Additive models