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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 1301–1350 of 2050 papers

TitleStatusHype
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
Behavioral analysis of support vector machine classifier with Gaussian kernel and imbalanced data—0
Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional DataCode0
Robust pricing and hedging via neural SDEsCode1
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and DatasetsCode1
Estimating Generalization under Distribution Shifts via Domain-Invariant RepresentationsCode1
Learning the Markov order of paths in a network—0
Deep learning for scene recognition from visual data: a survey—0
Learning with tree tensor networks: complexity estimates and model selection—0
In Search of Lost Domain GeneralizationCode1
Surveying Off-Board and Extra-Vehicular Monitoring and Progress Towards Pervasive Diagnostics—0
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)Code0
The huge Package for High-dimensional Undirected Graph Estimation in R—0
Statistical inference of assortative community structures—0
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels—0
Model family selection for classification using Neural Decision Trees—0
Open Problem: Model Selection for Contextual Bandits—0
Offline detection of change-points in the mean for stationary graph signalsCode0
Selecting Diverse Models for Scientific Insight—0
Towards an Unsupervised Method for Model Selection in Few-Shot Learning—0
Hidden Markov Models Applied To Intraday Momentum Trading With Side Information—0
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification—0
Assumption-lean inference for generalised linear model parametersCode1
Selecting the Number of Clusters K with a Stability Trade-off: an Internal Validation CriterionCode1
TensorFlow with user friendly Graphical Framework for object detection APICode1
Regret Balancing for Bandit and RL Model Selection—0
Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining—0
Virtual Reference Feedback Tuning with data-driven reference model selection—0
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach—0
Speedy Performance Estimation for Neural Architecture SearchCode0
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective—0
Rate-adaptive model selection over a collection of black-box contextual bandit algorithms—0
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits—0
Fuzzy c-Means Clustering for Persistence DiagramsCode1
DGSAC: Density Guided Sampling and Consensus—0
Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction—0
Variational Inference and Learning of Piecewise-linear Dynamical Systems—0
Learning Opinion Dynamics From Social TracesCode1
Unsupervised Discretization by Two-dimensional MDL-based HistogramCode0
Uncertainty Based Camera Model SelectionCode1
Quantized Neural Networks: Characterization and Holistic Optimization—0
Sig-SDEs model for quantitative finance—0
Solution Path Algorithm for Twin Multi-class Support Vector MachineCode0
Revealing consensus and dissensus between network partitions—0
Selective Inference for Latent Block Models—0
Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model—0
Learning Equations from Biological Data with Limited Time SamplesCode0
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law—0
Marginal likelihood computation for model selection and hypothesis testing: an extensive review—0
Forward utilities and Mean-field games under relative performance concerns—0
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