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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 2001–2050 of 2050 papers

TitleStatusHype
Leveraging Predictive Equivalence in Decision TreesCode0
Variational Resampling Based Assessment of Deep Neural Networks under Distribution ShiftCode0
On the cross-validation bias due to unsupervised pre-processingCode0
DECODE: Domain-aware Continual Domain Expansion for Motion PredictionCode0
Data-driven discovery of PDEs in complex datasetsCode0
Spatio-temporal Bayesian On-line Changepoint Detection with Model SelectionCode0
The Topology and Geometry of Neural RepresentationsCode0
Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method GenerationCode0
Approximate Cross-validation: Guarantees for Model Assessment and SelectionCode0
Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodelCode0
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learningCode0
Trained Models Tell Us How to Make Them Robust to Spurious Correlation without Group AnnotationCode0
On the Sample Complexity of Graphical Model Selection for Non-Stationary ProcessesCode0
LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot SystemsCode0
The use of cross validation in the analysis of designed experimentsCode0
Bayesian Neural Networks at Finite TemperatureCode0
Speech Enhancement with Zero-Shot Model SelectionCode0
Revisiting Bellman Errors for Offline Model SelectionCode0
Data-driven Advice for Applying Machine Learning to Bioinformatics ProblemsCode0
A Convex Framework for Confounding Robust InferenceCode0
Speedy Performance Estimation for Neural Architecture SearchCode0
When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairnessCode0
Bayesian Joint Spike-and-Slab Graphical LassoCode0
A multiple testing framework for diagnostic accuracy studies with co-primary endpointsCode0
DAGGER: A sequential algorithm for FDR control on DAGsCode0
Cross-Validation with ConfidenceCode0
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya UrnsCode0
AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive ModellingCode0
Cross-Validated Off-Policy EvaluationCode0
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical ModelsCode0
Stability selection enables robust learning of partial differential equations from limited noisy dataCode0
Risk Controlled Image RetrievalCode0
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference ModelsCode0
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer VisionCode0
Machine learning for sports betting: should model selection be based on accuracy or calibration?Code0
Batch Value-function Approximation with Only RealizabilityCode0
Machine learning in policy evaluation: new tools for causal inferenceCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
mage based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimizationCode0
Making Tree Ensembles Interpretable: A Bayesian Model Selection ApproachCode0
Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical ModelCode0
Transformers as Algorithms: Generalization and Stability in In-context LearningCode0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
Statistical Inference for Sequential Feature Selection after Domain AdaptationCode0
Transformers for Green Semantic Communication: Less Energy, More SemanticsCode0
Bayesian Inference of Minimally Complex Models with Interactions of Arbitrary OrderCode0
OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter OptimizationCode0
CHARDA: Causal Hybrid Automata Recovery via Dynamic AnalysisCode0
Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space ModelsCode0
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