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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 601–625 of 2050 papers

TitleStatusHype
Deep Active Learning with Adaptive AcquisitionCode0
Differentiable Model Selection for Ensemble LearningCode0
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect EstimationCode0
Decomposing Gaussians with Unknown CovarianceCode0
DECODE: Domain-aware Continual Domain Expansion for Motion PredictionCode0
Hybrid safe-strong rules for efficient optimization in lasso-type problemsCode0
Entity Set Search of Scientific Literature: An Unsupervised Ranking ApproachCode0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
AALF: Almost Always Linear ForecastingCode0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly DetectionCode0
Know2Vec: A Black-Box Proxy for Neural Network RetrievalCode0
Don't Waste Your Time: Early Stopping Cross-ValidationCode0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
Large Scale Correlation Clustering OptimizationCode0
LASSO-ODE: A framework for mechanistic model identifiability and selection in disease transmission modelingCode0
Effective Stabilized Self-Training on Few-Labeled Graph DataCode0
Effects of sampling skewness of the importance-weighted risk estimator on model selectionCode0
Automatic Gradient BoostingCode0
Driver Identification by an Ensemble of CNNs Obtained from Majority-Voting Model SelectionCode0
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learningCode0
Learning Equations from Biological Data with Limited Time SamplesCode0
DSV: An Alignment Validation Loss for Self-supervised Outlier Model SelectionCode0
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