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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 801–810 of 2050 papers

TitleStatusHype
UniASM: Binary Code Similarity Detection without Fine-tuningCode1
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian QuadratureCode1
Estimation of Heterogeneous Treatment Effects Using a Conditional Moment Based Approach—0
TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection—0
Which is the best model for my data?—0
Improving Group Lasso for high-dimensional categorical data—0
Post-Selection Confidence Bounds for Prediction PerformanceCode0
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter TuningCode1
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular dataCode1
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal TransportCode0
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