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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 901–925 of 2050 papers

TitleStatusHype
Bayesian Boosting for Linear Mixed Models—0
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness—0
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets—0
How to select predictive models for causal inference?—0
How to Select Pre-Trained Code Models for Reuse? A Learning Perspective—0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
hv-Block Cross Validation is not a BIBD: a Note on the Paper by Jeff Racine (2000)—0
Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model—0
Bayesian Anomaly Detection and Classification—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework—0
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data—0
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets—0
A Local Information Criterion for Dynamical Systems—0
Meta-Evaluating Local LLMs: Rethinking Performance Metrics for Serious Games—0
Designing Ecosystems of Intelligence from First Principles—0
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory—0
ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets—0
A Nested Weighted Tchebycheff Multi-Objective Bayesian Optimization Approach for Flexibility of Unknown Utopia Estimation in Expensive Black-box Design Problems—0
Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net—0
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach—0
Adaptive model selection in photonic reservoir computing by reinforcement learning—0
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