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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 1041–1050 of 2050 papers

TitleStatusHype
UniPELT: A Unified Framework for Parameter-Efficient Language Model TuningCode1
Finding Materialized Models for Model ReuseCode0
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings—0
MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in BuildingsCode0
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random ForestsCode1
Variance function estimation in regression model via aggregation procedures—0
Post-hoc Models for Performance Estimation of Machine Learning Inference—0
Empirical Quantitative Analysis of COVID-19 Forecasting Models—0
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