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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 1376–1400 of 2050 papers

TitleStatusHype
Leveraging LLMs for MT in Crisis Scenarios: a blueprint for low-resource languages—0
LEVIS: Large Exact Verifiable Input Spaces for Neural Networks—0
Lexical Bias In Essay Level Prediction—0
Lifelong Bayesian Optimization—0
A Geometric Modeling of Occam's Razor in Deep Learning—0
Likelihood Adaptively Modified Penalties—0
Limits of Model Selection under Transfer Learning—0
LIMSI@CoNLL'17: UD Shared Task—0
Linear Bandits with Memory: from Rotting to Rising—0
Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior—0
LLM4DS: Evaluating Large Language Models for Data Science Code Generation—0
LLMProxy: Reducing Cost to Access Large Language Models—0
LM-BIC Model Selection in Semiparametric Models—0
Logistic principal component analysis via non-convex singular value thresholding—0
Logits-Constrained Framework with RoBERTa for Ancient Chinese NER—0
Lookback for Learning to Branch—0
"Look Ma, No Hands!" A Parameter-Free Topic Model—0
Loss function based second-order Jensen inequality and its application to particle variational inference—0
Loss-guided Stability Selection—0
Lossy Compression with Distortion Constrained Optimization—0
Lower Bounds on Active Learning for Graphical Model Selection—0
Luria-Delbruck, revisited: The classic experiment does not rule out Lamarckian evolution—0
Machine Learning: a Lecture Note—0
Machine Learning-Assisted Analysis of Small Angle X-ray Scattering—0
Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation—0
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