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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 81–90 of 2050 papers

TitleStatusHype
Topological model selection: a case-study in tumour-induced angiogenesis—0
Transfer Learning via Auxiliary Labels with Application to Cold-Hardiness Prediction—0
Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs?—0
Physics-Aware Initialization Refinement in Code-Aided EM for Blind Channel Estimation—0
Meta-Evaluating Local LLMs: Rethinking Performance Metrics for Serious Games—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability—0
What the HellaSwag? On the Validity of Common-Sense Reasoning BenchmarksCode0
Robust Social Planning—0
M-Prometheus: A Suite of Open Multilingual LLM JudgesCode5
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