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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 101–150 of 2050 papers

TitleStatusHype
Toward Unsupervised Outlier Model SelectionCode1
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian QuadratureCode1
UniASM: Binary Code Similarity Detection without Fine-tuningCode1
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular dataCode1
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter TuningCode1
XAI for transparent wind turbine power curve modelsCode1
Graph Anomaly Detection with Unsupervised GNNsCode1
PARAGEN : A Parallel Generation ToolkitCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
Unsupervised Model Selection for Time-series Anomaly DetectionCode1
Entropic Descent Archetypal Analysis for Blind Hyperspectral UnmixingCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
clusterBMA: Bayesian model averaging for clusteringCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
HyperImpute: Generalized Iterative Imputation with Automatic Model SelectionCode1
Unsupervised Image Representation Learning with Deep Latent ParticlesCode1
Time Series Anomaly Detection via Reinforcement Learning-Based Model SelectionCode1
NICO++: Towards Better Benchmarking for Domain GeneralizationCode1
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
Monitored Distillation for Positive Congruent Depth CompletionCode1
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
Rich Feature Construction for the Optimization-Generalization DilemmaCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think AgainCode1
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification TasksCode1
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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