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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 801–850 of 2050 papers

TitleStatusHype
Green Runner: A tool for efficient model selection from model repositories—0
Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID—0
Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks—0
Learning Relevant Contextual Variables Within Bayesian OptimizationCode0
Clustering Indices based Automatic Classification Model SelectionCode0
Unraveling Cold Start Enigmas in Predictive Analytics for OTT Media: Synergistic Meta-Insights and Multimodal Ensemble Mastery—0
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model—0
Ranking & Reweighting Improves Group Distributional Robustness—0
Boldness-Recalibration for Binary Event Predictions—0
fairml: A Statistician's Take on Fair Machine Learning Modelling—0
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces—0
Limits of Model Selection under Transfer Learning—0
ALMERIA: Boosting pairwise molecular contrasts with scalable methods—0
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits—0
Sparse Private LASSO Logistic Regression—0
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs—0
Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022—0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Efficient Deep Reinforcement Learning Requires Regulating Overfitting—0
An Offline Metric for the Debiasedness of Click ModelsCode0
Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings—0
Modeling Transient Changes in Circadian Rhythms—0
The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges—0
Priors for symbolic regressionCode0
How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of DocumentsCode0
A principled approach to model validation in domain generalizationCode0
Model Validation and Selection in Metabolic Flux Analysis and Flux Balance Analysis—0
Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
Bootstrap based asymptotic refinements for high-dimensional nonlinear models—0
Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation—0
Deploying Offline Reinforcement Learning with Human Feedback—0
Solar Power Prediction Using Machine Learning—0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning—0
Machine learning for sports betting: should model selection be based on accuracy or calibration?Code0
A variational synthesis of evolutionary and developmental dynamics—0
Training Machine Learning Models to Characterize Temporal Evolution of Disadvantaged Communities—0
Ensemble Reinforcement Learning: A Survey—0
Online simulator-based experimental design for cognitive model selectionCode0
Bayesian CART models for insurance claims frequency—0
In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised LearningCode0
Hyperparameter Tuning and Model Evaluation in Causal Effect EstimationCode0
A Vision for Semantically Enriched Data Science—0
FedScore: A privacy-preserving framework for federated scoring system developmentCode0
A novel efficient Multi-view traffic-related object detection framework—0
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality—0
Pseudo-Labeling for Kernel Ridge Regression under Covariate ShiftCode0
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles—0
Evaluating Representations with Readout Model Switching—0
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