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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1026–1050 of 2050 papers

TitleStatusHype
A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization—0
Predictor Selection for Synthetic Controls—0
On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging—0
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work—0
Mixture Components Inference for Sparse Regression: Introduction and Application for Estimation of Neuronal Signal from fMRI BOLD—0
Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management—0
Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications—0
Bayesian Spatial Predictive Synthesis—0
Geometric and Topological Inference for Deep Representations of Complex Networks—0
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows—0
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion—0
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis—0
Gaussian Process-based Spatial Reconstruction of Electromagnetic fields—0
A study on the distribution of social biases in self-supervised learning visual models—0
Regularized Bilinear Discriminant Analysis for Multivariate Time Series Data—0
Ensemble Method for Estimating Individualized Treatment Effects—0
Exploratory Hidden Markov Factor Models for Longitudinal Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae—0
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition—0
Efficient Distributed DNNs in the Mobile-edge-cloud Continuum—0
Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluation—0
Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks—0
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants—0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold RegressionCode0
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