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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
Can We Characterize Tasks Without Labels or Features?Code1
Learned harmonic mean estimation of the marginal likelihood with normalizing flowsCode1
Automated Machine Learning in InsuranceCode1
LogME: Practical Assessment of Pre-trained Models for Transfer LearningCode1
cegpy: Modelling with Chain Event Graphs in PythonCode1
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain GeneralizationCode1
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
clusterBMA: Bayesian model averaging for clusteringCode1
Machine Learning for Dynamic Resource Allocation in Network Function VirtualizationCode1
mikropml: User-Friendly R Package for Supervised Machine Learning PipelinesCode1
Estimating Generalization under Distribution Shifts via Domain-Invariant RepresentationsCode1
Triple equivalence for the emergence of biological intelligenceCode1
abess: A Fast Best Subset Selection Library in Python and RCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
NICO++: Towards Better Benchmarking for Domain GeneralizationCode1
NLP-ADBench: NLP Anomaly Detection BenchmarkCode1
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image SegmentationCode1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
PARAGEN : A Parallel Generation ToolkitCode1
Duality Diagram Similarity: a generic framework for initialization selection in task transfer learningCode1
Population Based Training of Neural NetworksCode1
ProbVLM: Probabilistic Adapter for Frozen Vision-Language ModelsCode1
DeSocial: Blockchain-based Decentralized Social NetworksCode1
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster ManagementCode1
Empirical evaluation of scoring functions for Bayesian network model selectionCode1
Deep learning for dynamic graphs: models and benchmarksCode1
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark StudyCode1
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical CareCode1
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models CascadeCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
DriveML: An R Package for Driverless Machine LearningCode1
Assumption-lean inference for generalised linear model parametersCode1
A stacked DCNN to predict the RUL of a turbofan engineCode1
A network approach to topic modelsCode1
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
Entropic Descent Archetypal Analysis for Blind Hyperspectral UnmixingCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD GeneralizationCode1
An information criterion for automatic gradient tree boostingCode1
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing dataCode1
Data Models for Dataset Drift Controls in Machine Learning With Optical ImagesCode1
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