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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 1926–1950 of 2050 papers

TitleStatusHype
Solar Flare Forecast: A Comparative Analysis of Machine Learning Algorithms for Solar Flare Class PredictionCode0
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source LayoutCode0
LaF: Labeling-Free Model Selection for Automated Deep Neural Network ReusingCode0
Solution Path Algorithm for Twin Multi-class Support Vector MachineCode0
Best Arm Identification for Stochastic Rising BanditsCode0
Changing the Kernel During Training Leads to Double Descent in Kernel RegressionCode0
One For All & All For One: Bypassing Hyperparameter Tuning with Model Averaging For Cross-Lingual TransferCode0
LaPLACE: Probabilistic Local Model-Agnostic Causal ExplanationsCode0
Deep Generalized Method of Moments for Instrumental Variable AnalysisCode0
Cold Case: The Lost MNIST DigitsCode0
The Merging Path Plot: adaptive fusing of k-groups with likelihood-based model selectionCode0
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A SurveyCode0
Clustering Indices based Automatic Classification Model SelectionCode0
A Realistic Protocol for Evaluation of Weakly Supervised Object LocalizationCode0
Large Scale Correlation Clustering OptimizationCode0
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature ExtractorsCode0
LASSO-ODE: A framework for mechanistic model identifiability and selection in disease transmission modelingCode0
A principled approach to model validation in domain generalizationCode0
Last Layer Marginal Likelihood for Invariance LearningCode0
Warlock: an automated computational workflow for simulating spatially structured tumour evolutionCode0
Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre studyCode0
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal InferenceCode0
LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously ThoughtCode0
Deeper Insights into Graph Convolutional Networks for Semi-Supervised LearningCode0
Adaptive Concentration of Regression Trees, with Application to Random ForestsCode0
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