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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 1001–1025 of 2050 papers

TitleStatusHype
Fair Community Detection and Structure Learning in Heterogeneous Graphical Models—0
Automatic Unsupervised Outlier Model Selection—0
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection—0
Hierarchical clustering: visualization, feature importance and model selectionCode0
Conceptually Diverse Base Model Selection for Meta-Learners in Concept Drifting Data StreamsCode0
Pessimistic Model Selection for Offline Deep Reinforcement Learning—0
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors—0
Learning Conditional Invariance through Cycle ConsistencyCode0
A stacked DCNN to predict the RUL of a turbofan engineCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology—0
Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior—0
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning—0
Towards Better Citation Intent Classification—0
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion—0
Machine Learning-Assisted Analysis of Small Angle X-ray Scattering—0
Optimizing Unlicensed Coexistence Network Performance Through Data Learning—0
A Rule-Based Epidemiological Modelling Framework—0
Scalable Diverse Model Selection for Accessible Transfer LearningCode1
Guided Sampling-based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis—0
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical CareCode1
Joint Inference for Neural Network Depth and Dropout RegularizationCode0
Universal and data-adaptive algorithms for model selection in linear contextual bandits—0
We Need to Talk About train-dev-test SplitsCode0
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