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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 761–770 of 2050 papers

TitleStatusHype
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation—0
Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders—0
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—0
On the Existence of Simpler Machine Learning Models—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—0
Collab: Controlled Decoding using Mixture of Agents for LLM Alignment—0
A Strong Baseline for Batch Imitation Learning—0
Cognito: Automated Feature Engineering for Supervised Learning—0
A Statistical Theory of Deep Learning via Proximal Splitting—0
A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection—0
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