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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 151–175 of 2050 papers

TitleStatusHype
CNN Model & Tuning for Global Road Damage DetectionCode1
An Information-theoretic Approach to Distribution ShiftsCode1
GeoGalactica: A Scientific Large Language Model in GeoscienceCode1
Graph Anomaly Detection with Unsupervised GNNsCode1
BERTScore: Evaluating Text Generation with BERTCode1
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
BayesOpt Adversarial AttackCode1
Additive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great BritainCode1
In Search of Lost Domain GeneralizationCode1
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster ManagementCode1
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter TuningCode1
Laplace Redux -- Effortless Bayesian Deep LearningCode1
Learned harmonic mean estimation of the marginal likelihood with normalizing flowsCode1
Learning Opinion Dynamics From Social TracesCode1
A stacked DCNN to predict the RUL of a turbofan engineCode1
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR ImagesCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration ErrorCode1
Can We Characterize Tasks Without Labels or Features?Code1
Machine-Guided Discovery of a Real-World Rogue Wave ModelCode1
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
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