SOTAVerified

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
clusterBMA: Bayesian model averaging for clusteringCode1
Interpretable multiclass classification by MDL-based rule listsCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Laplace Redux -- Effortless Bayesian Deep LearningCode1
Learned harmonic mean estimation of the marginal likelihood with normalizing flowsCode1
Learning Opinion Dynamics From Social TracesCode1
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster ManagementCode1
LogME: Practical Assessment of Pre-trained Models for Transfer LearningCode1
LOVM: Language-Only Vision Model SelectionCode1
Machine-Guided Discovery of a Real-World Rogue Wave ModelCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Modeling the Second Player in Distributionally Robust OptimizationCode1
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration ErrorCode1
Monitored Distillation for Positive Congruent Depth CompletionCode1
cegpy: Modelling with Chain Event Graphs in PythonCode1
NICO++: Towards Better Benchmarking for Domain GeneralizationCode1
Noether's razor: Learning Conserved QuantitiesCode1
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
Counterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline EvaluationCode1
OTCE: A Transferability Metric for Cross-Domain Cross-Task RepresentationsCode1
An Information-theoretic Approach to Distribution ShiftsCode1
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science DomainsCode1
Population Based Training of Neural NetworksCode1
ProbVLM: Probabilistic Adapter for Frozen Vision-Language ModelsCode1
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion ModelsCode1
Automating Outlier Detection via Meta-LearningCode1
BarcodeBERT: Transformers for Biodiversity AnalysisCode1
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models CascadeCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian QuadratureCode1
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
BayesOpt Adversarial AttackCode1
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM EvaluationCode1
BERTScore: Evaluating Text Generation with BERTCode1
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR ImagesCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
A network approach to topic modelsCode1
Can We Characterize Tasks Without Labels or Features?Code1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
CNN Model & Tuning for Global Road Damage DetectionCode1
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular dataCode1
An information criterion for automatic gradient tree boostingCode1
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
Assumption-lean inference for generalised linear model parametersCode1
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