Papers Additive models
“Additive models” 태그가 달린 논문 252편 · 필터 해제
The Most Important Features in Generalized Additive Models Might Be Groups of Features
While analyzing the importance of features has become ubiquitous in interpretable machine learning, the joint signal from a group of related features is sometimes overlooked or inadvertently excluded. Neglecting the join…
Additive modelsInterpretable Machine LearningAn Optimized Franz-Parisi Criterion and its Equivalence with SQ Lower Bounds
Bandeira et al. (2022) introduced the Franz-Parisi (FP) criterion for characterizing the computational hard phases in statistical detection problems. The FP criterion, based on an annealed version of the celebrated Franz…
Additive modelsLearning Joint Interventional Effects from Single-Variable Interventions in Additive Models
Estimating causal effects of joint interventions on multiple variables is crucial in many domains, but obtaining data from such simultaneous interventions can be challenging. Our study explores how to learn joint interve…
Additive modelsneuralGAM: An R Package for Fitting Generalized Additive Neural Networks
Nowadays, Neural Networks are considered one of the most effective methods for various tasks such as anomaly detection, computer-aided disease detection, or natural language processing. However, these networks suffer fro…
Additive modelsAnomaly DetectionNavigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users
The Rashomon effect describes the observation that in machine learning (ML) multiple models often achieve similar predictive performance while explaining the underlying relationships in different ways. This observation h…
Additive modelsInterpretable Machine LearningMulti-Armed BanditsRecursive Identification of Structured Systems: An Instrumental-Variable Approach Applied to Mechanical Systems
Online system identification algorithms are widely used for monitoring, diagnostics and control by continuously adapting to time-varying dynamics. Typically, these algorithms consider a model structure that lacks parsimo…
Additive modelsparameter estimationChallenges in interpretability of additive models
We review generalized additive models as a type of ``transparent'' model that has recently seen renewed interest in the deep learning community as neural additive models. We highlight multiple types of nonidentifiability…
Additive modelsAdaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories
Longitudinal biomedical studies monitor individuals over time to capture dynamics in brain development, disease progression, and treatment effects. However, estimating trajectories of brain biomarkers is challenging due …
Additive modelsBenchmarkingUncertainty QuantificationIdentification of additive multivariable continuous-time systems
Multivariable parametric models are critical for designing, controlling, and optimizing the performance of engineered systems. The main objective of this paper is to develop a parametric identification strategy that deli…
Additive modelsAutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting
Electricity demand forecasting is key to ensuring that supply meets demand lest the grid would blackout. Reliable short-term forecasts may be obtained by combining a Generalized Additive Models (GAM) with a State-Space m…
Additive modelsAutoMLDemand ForecastingModel Selection+1MT-NAM: An Efficient and Adaptive Model for Epileptic Seizure Detection
Enhancing the accuracy and efficiency of machine learning algorithms employed in neural interface systems is crucial for advancing next-generation intelligent therapeutic devices. However, current systems often utilize b…
Additive modelsEEGSeizure DetectionSensitivity+1Gradient-free stochastic optimization for additive models
We address the problem of zero-order optimization from noisy observations for an objective function satisfying the Polyak-{\L}ojasiewicz or the strong convexity condition. Additionally, we assume that the objective funct…
Additive modelsStochastic OptimizationInstaSHAP: Interpretable Additive Models Explain Shapley Values Instantly
In recent years, the Shapley value and SHAP explanations have emerged as one of the most dominant paradigms for providing post-hoc explanations of black-box models. Despite their well-founded theoretical properties, many…
Additive modelsComputational EfficiencyForecasting time series with constraints
Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have incorporated linear constraints into learn…
Additive modelsBenchmarkingDemand ForecastingTime Series+1Normative Cerebral Perfusion Across the Lifespan
Cerebral perfusion plays a crucial role in maintaining brain function and is tightly coupled with neuronal activity. While previous studies have examined cerebral perfusion trajectories across development and aging, prec…
Additive models$\texttt{LucidAtlas}$: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representations
The goal of this work is to develop principled techniques to extract information from high dimensional data sets with complex dependencies in areas such as medicine that can provide insight into individual as well as pop…
Additive modelsscientific discoveryCausal Additive Models with Unobserved Causal Paths and Backdoor Paths
Causal additive models have been employed as tractable yet expressive frameworks for causal discovery involving hidden variables. State-of-the-art methodologies suggest that determining the causal relationship between a …
Additive modelsCausal DiscoveryregressionVariable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age
Many problems within personalized medicine and digital health rely on the analysis of continuous-time functional biomarkers and other complex data structures emerging from high-resolution patient monitoring. In this cont…
Additive modelsregressionVariable SelectionHow Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data
Health outcomes depend on complex environmental and sociodemographic factors whose effects change over location and time. Only recently has fine-grained spatial and temporal data become available to study these effects, …
Additive modelsInterpretable Machine LearningHigh-Dimensional Markov-switching Ordinary Differential Processes
We investigate the parameter recovery of Markov-switching ordinary differential processes from discrete observations, where the differential equations are nonlinear additive models. This framework has been widely applied…
Additive models