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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 876–900 of 2050 papers

TitleStatusHype
Guided Recommendation for Model Fine-Tuning—0
BiasBed - Rigorous Texture Bias EvaluationCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
Bayesian Interpolation with Deep Linear Networks—0
Choosing the Number of Topics in LDA Models -- A Monte Carlo Comparison of Selection CriteriaCode0
Fast and fully-automated histograms for large-scale data sets—0
Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks—0
An Information-Theoretic Approach to Transferability in Task Transfer Learning—0
On the Complexity of Representation Learning in Contextual Linear Bandits—0
Dominant Drivers of National Inflation—0
Optimal Model Selection in RDD and Related Settings Using Placebo Zones—0
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation—0
Stochastic Rising BanditsCode0
Designing Ecosystems of Intelligence from First Principles—0
Hierarchical Model Selection for Graph Neural Netoworks—0
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective—0
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting—0
Direct-Effect Risk Minimization for Domain GeneralizationCode0
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance—0
The smooth output assumption, and why deep networks are better than wide ones—0
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing—0
BiasBed -- Rigorous Texture Bias EvaluationCode0
Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks—0
MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models—0
Exploring validation metrics for offline model-based optimisation with diffusion modelsCode0
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