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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 626–650 of 2050 papers

TitleStatusHype
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and CalibrationCode0
The Interpolating Information Criterion for Overparameterized Models—0
Sparsified Simultaneous Confidence Intervals for High-Dimensional Linear Models—0
DataAssist: A Machine Learning Approach to Data Cleaning and Preparation—0
Risk Controlled Image RetrievalCode0
DSV: An Alignment Validation Loss for Self-supervised Outlier Model SelectionCode0
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks—0
Online Laplace Model Selection Revisited—0
Deep learning for dynamic graphs: models and benchmarksCode1
GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts—0
Bayesian taut splines for estimating the number of modes—0
Action-State Dependent Dynamic Model Selection—0
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?—0
Learning Lie Group Symmetry Transformations with Neural NetworksCode0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
ProbVLM: Probabilistic Adapter for Frozen Vision-Language ModelsCode1
Proximal nested sampling with data-driven priors for physical scientistsCode1
Learned harmonic mean estimation of the marginal likelihood with normalizing flowsCode1
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Confidence-based Ensembles of End-to-End Speech Recognition Models—0
Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning—0
Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications—0
Challenges and Opportunities in Improving Worst-Group Generalization in Presence of Spurious FeaturesCode1
Human Limits in Machine Learning: Prediction of Plant Phenotypes Using Soil Microbiome DataCode0
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts—0
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