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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 151–200 of 2050 papers

TitleStatusHype
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science DomainsCode1
An Information-theoretic Approach to Distribution ShiftsCode1
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
DriveML: An R Package for Driverless Machine LearningCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
BERTScore: Evaluating Text Generation with BERTCode1
Additive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great BritainCode1
Evaluating Language Models as Synthetic Data GeneratorsCode1
Evaluating Weakly Supervised Object Localization Methods RightCode1
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and DatasetsCode1
A comparison of methods for model selection when estimating individual treatment effectsCode1
Exploiting BERT for End-to-End Aspect-based Sentiment AnalysisCode1
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models CascadeCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
BayesOpt Adversarial AttackCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
Hologram Reasoning for Solving Algebra Problems with Geometry DiagramsCode1
BarcodeBERT: Transformers for Biodiversity AnalysisCode1
HyperImpute: Generalized Iterative Imputation with Automatic Model SelectionCode1
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANsCode1
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive RegularizersCode1
Adversarial Branch Architecture Search for Unsupervised Domain AdaptationCode1
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label LearningCode1
Laplace Redux -- Effortless Bayesian Deep LearningCode1
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian QuadratureCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter TuningCode1
LIBTwinSVM: A Library for Twin Support Vector MachinesCode1
Machine-Guided Discovery of a Real-World Rogue Wave ModelCode1
Machine Learning for Dynamic Resource Allocation in Network Function VirtualizationCode1
A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation TasksCode1
Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and ClassificationCode1
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
Automating Outlier Detection via Meta-LearningCode1
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion ModelsCode1
Online Active Model Selection for Pre-trained ClassifiersCode1
Counterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline EvaluationCode1
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular dataCode1
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD GeneralizationCode1
PARAGEN : A Parallel Generation ToolkitCode1
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of PovertyCode1
Population Based Training of Neural NetworksCode1
Quantifying & Modeling Multimodal Interactions: An Information Decomposition FrameworkCode1
One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image SegmentationCode1
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