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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 441–450 of 2050 papers

TitleStatusHype
Learning under Singularity: An Information Criterion improving WBIC and sBIC—0
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM EvaluationCode1
Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Model Assessment and Selection under Temporal Distribution ShiftCode0
LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied AgentsCode2
Online Foundation Model Selection in Robotics—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
Compressive Recovery of Signals Defined on Perturbed Graphs—0
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