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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 301–350 of 2050 papers

TitleStatusHype
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition—0
A Sparse Bayesian Learning Algorithm for Estimation of Interaction Kernels in Motsch-Tadmor Model—0
A simple application of FIC to model selection—0
Unsupervised Model Selection for Variational Disentangled Representation Learning—0
A Sentiment Analysis of Medical Text Based on Deep Learning—0
A Rule-Based Epidemiological Modelling Framework—0
A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI—0
A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: application to porcine aortic valves—0
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels—0
Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor—0
Arrival Time Prediction for Autonomous Shuttle Services in the Real World: Evidence from Five Cities—0
Agreement-based Learning—0
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning—0
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
Aggregation of Affine Estimators—0
A coupled-mechanisms modelling framework for neurodegeneration—0
A Review of Change of Variable Formulas for Generative Modeling—0
A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods—0
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow—0
A Regret-Variance Trade-Off in Online Learning—0
Area under the ROC Curve has the Most Consistent Evaluation for Binary Classification—0
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective—0
A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees—0
A Bayesian Approach to Network Modularity—0
Classification with Scattering Operators—0
Closing the gap between open-source and commercial large language models for medical evidence summarization—0
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
Choice modelling in the age of machine learning - discussion paper—0
Block-Term Tensor Decomposition Model Selection and Computation: The Bayesian Way—0
A Systematic Analysis of Base Model Choice for Reward Modeling—0
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation—0
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study—0
A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models—0
A Practitioner's Guide to Automatic Kernel Search for Gaussian Processes in Battery Applications—0
Approximation of Intractable Likelihood Functions in Systems Biology via Normalizing Flows—0
A first econometric analysis of the CRIX family—0
Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI—0
Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework—0
Approximate Leave-one-out Cross Validation for Regression with _1 Regularizers (extended version)—0
A Federated Learning Framework for Non-Intrusive Load Monitoring—0
Application of Machine Learning in Stock Market Forecasting: A Case Study of Disney Stock—0
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines—0
A Powerful Subvector Anderson Rubin Test in Linear Instrumental Variables Regression with Conditional Heteroskedasticity—0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection—0
Adversarial Negotiation Dynamics in Generative Language Models—0
Causal Q-Aggregation for CATE Model Selection—0
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion—0
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach—0
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