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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 1851–1900 of 2050 papers

TitleStatusHype
Efficient Bias Mitigation Without Privileged Information—0
Efficient Cross-Validation for Semi-Supervised Learning—0
Efficient Deep Reinforcement Learning Requires Regulating Overfitting—0
Efficient Distributed Estimation of Inverse Covariance Matrices—0
Efficient Estimation of the number of neighbours in Probabilistic K Nearest Neighbour Classification—0
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming—0
Efficient Model Compression for Bayesian Neural Networks—0
Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning—0
Efficient Model Selection for Time Series Forecasting via LLMs—0
Efficient model selection in switching linear dynamic systems by graph clustering—0
Efficient Sequential Decision Making with Large Language Models—0
Efficient speech detection in environmental audio using acoustic recognition and knowledge distillation—0
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields—0
EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis—0
Empirical analysis in limit order book modeling for Nikkei 225 Stocks with Cox-type intensities—0
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection—0
Empirical Quantitative Analysis of COVID-19 Forecasting Models—0
Empowering Agricultural Insights: RiceLeafBD - A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique—0
Encoding-dependent generalization bounds for parametrized quantum circuits—0
End-to-End Edge AI Service Provisioning Framework in 6G ORAN—0
Energy-Aware Dynamic Neural Inference—0
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants—0
Efficient Distributed DNNs in the Mobile-edge-cloud Continuum—0
Enhancing Certifiable Robustness via a Deep Model Ensemble—0
Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective—0
Enhancing the Power of OOD Detection via Sample-Aware Model Selection—0
Ensemble Method for Estimating Individualized Treatment Effects—0
Ensemble Reinforcement Learning: A Survey—0
Entropy-based Characterization of Modeling Constraints—0
Epidemic Dynamics via Wavelet Theory and Machine Learning, with Applications to Covid-19—0
Episodic memory for continual model learning—0
ER2Score: LLM-based Explainable and Customizable Metric for Assessing Radiology Reports with Reward-Control Loss—0
Error Reduction from Stacked Regressions—0
Estimating Optimal Policy Value in General Linear Contextual Bandits—0
Estimating Real Log Canonical Thresholds—0
Estimating Stable Fixed Points and Langevin Potentials for Financial Dynamics—0
Estimating the Number of Components in Finite Mixture Models via Variational Approximation—0
Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity—0
Estimation of Heterogeneous Treatment Effects Using a Conditional Moment Based Approach—0
Estimation of Local Average Treatment Effect by Data Combination—0
Estimation vs Metrics: is QE Useful for MT Model Selection?—0
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors—0
How have German University Tuition Fees Affected Enrollment Rates: Robust Model Selection and Design-based Inference in High-Dimensions—0
Evaluating Gender Bias in Large Language Models—0
Evaluating Generalization and Representation Stability in Small LMs via Prompting, Fine-Tuning and Out-of-Distribution Prompts—0
Evaluating Meta-Regression Techniques: A Simulation Study on Heterogeneity in Location and Time—0
Evaluating Representations with Readout Model Switching—0
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion—0
Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR—0
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?—0
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