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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 526–550 of 2050 papers

TitleStatusHype
Dimension Independent Generalization Error by Stochastic Gradient Descent—0
BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices—0
Best Practices for Text Annotation with Large Language Models—0
A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation—0
Best of many worlds: Robust model selection for online supervised learning—0
Best of Both Worlds Model Selection—0
BadJudge: Backdoor Vulnerabilities of LLM-as-a-Judge—0
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation—0
Non-asymptotic model selection in block-diagonal mixture of polynomial experts models—0
A deep learning based solution for construction equipment detection: from development to deployment—0
Benchmarking the rationality of AI decision making using the transitivity axiom—0
Anomaly Detection for an E-commerce Pricing System—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
Benchmarking Open-Source Large Language Models on Healthcare Text Classification Tasks—0
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets—0
Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images—0
Belief propagation for permutations, rankings, and partial orders—0
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition—0
Dimensionality Dependent PAC-Bayes Margin Bound—0
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle—0
Disentangling Factors of Variation Using Few Labels—0
Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts—0
Behavioral analysis of support vector machine classifier with Gaussian kernel and imbalanced data—0
Unsupervised Optimisation of GNNs for Node Clustering—0
Bayesian Variable Selection for Globally Sparse Probabilistic PCA—0
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