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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 126–150 of 2050 papers

TitleStatusHype
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
Invariance Learning in Deep Neural Networks with Differentiable Laplace ApproximationsCode1
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing dataCode1
On Evaluation Metrics for Graph Generative ModelsCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
A stacked DCNN to predict the RUL of a turbofan engineCode1
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical CareCode1
Scalable Diverse Model Selection for Accessible Transfer LearningCode1
Simple data balancing achieves competitive worst-group-accuracyCode1
mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in RCode1
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain GeneralizationCode1
abess: A Fast Best Subset Selection Library in Python and RCode1
Hydra: A System for Large Multi-Model Deep LearningCode1
UniPELT: A Unified Framework for Parameter-Efficient Language Model TuningCode1
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random ForestsCode1
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space DecompositionCode1
Laplace Redux -- Effortless Bayesian Deep LearningCode1
Can We Characterize Tasks Without Labels or Features?Code1
QuaPy: A Python-Based Framework for QuantificationCode1
An Information-theoretic Approach to Distribution ShiftsCode1
True Few-Shot Learning with Language ModelsCode1
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science DomainsCode1
mikropml: User-Friendly R Package for Supervised Machine Learning PipelinesCode1
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