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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 751–775 of 2050 papers

TitleStatusHype
Non-Bayesian Post-Model-Selection Estimation as Estimation Under Model Misspecification—0
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE DiscoveryCode0
Open, Closed, or Small Language Models for Text Classification?—0
No Regularization is Needed: An Efficient and Effective Model for Incomplete Label Distribution Learning—0
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance—0
A coupled-mechanisms modelling framework for neurodegeneration—0
Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review—0
A Review of Change of Variable Formulas for Generative Modeling—0
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities—0
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation—0
Predictive Modeling through Hyper-Bayesian Optimization—0
A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI—0
An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading—0
Learning Disentangled Discrete RepresentationsCode0
Rational kernel-based interpolation for complex-valued frequency response functions—0
Consistent model selection in the spiked Wigner model via AIC-type criteria—0
Anytime Model Selection in Linear BanditsCode0
Adaptive debiased machine learning using data-driven model selection techniques—0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Towards a performance analysis on pre-trained Visual Question Answering models for autonomous drivingCode0
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and CalibrationCode0
The Interpolating Information Criterion for Overparameterized Models—0
DataAssist: A Machine Learning Approach to Data Cleaning and Preparation—0
Risk Controlled Image RetrievalCode0
Sparsified Simultaneous Confidence Intervals for High-Dimensional Linear Models—0
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