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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 571–580 of 2050 papers

TitleStatusHype
Tuning Large language model for End-to-end Speech Translation—0
LaPLACE: Probabilistic Local Model-Agnostic Causal ExplanationsCode0
Dynamic Interpretability for Model Comparison via Decision RulesCode0
Structural Risk Minimization for Learning Nonlinear Dynamics—0
Wave-shape Function Model Order Estimation by Trigonometric Regression—0
Forecasting large collections of time series: feature-based methods—0
Pseudo Label Selection is a Decision Problem—0
A closer look at parameter identifiability, model selection and handling of censored data with Bayesian Inference in mathematical models of tumour growth—0
Big model only for hard audios: Sample dependent Whisper model selection for efficient inferencesCode0
A Convex Framework for Confounding Robust InferenceCode0
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