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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 1951–1975 of 2050 papers

TitleStatusHype
Deep Bayesian Multi-Target Learning for Recommender SystemsCode0
Recurrent Neural Networks for Fuzz Testing Web BrowsersCode0
A Deep Learning Method for Comparing Bayesian Hierarchical ModelsCode0
Variational Inference for Sparse Gaussian Process Modulated Hawkes ProcessCode0
Learning Conditional Invariance through Cycle ConsistencyCode0
Learning Counterfactual Representations for Estimating Individual Dose-Response CurvesCode0
A-DARTS: Stable Model Selection for Data Repair in Time SeriesCode0
Learning diffusion coefficients, kinetic parameters, and the number of underlying states from a multi-state diffusion process: robustness results and application to PDK1/PKCα, dynamicsCode0
Learning Disentangled Discrete RepresentationsCode0
Sparse Interaction Neighborhood Selection for Markov Random Fields via Reversible Jump and PseudoposteriorsCode0
Learning Equations for Extrapolation and ControlCode0
Learning Equations from Biological Data with Limited Time SamplesCode0
Additive interaction modelling using I-priorsCode0
Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical ModelsCode0
Online simulator-based experimental design for cognitive model selectionCode0
Sparsely Activated NetworksCode0
Multivariate rank via entropic optimal transport: sample efficiency and generative modelingCode0
Unsupervised Attention Mechanism across Neural Network LayersCode0
The Reciprocal Bayesian LASSOCode0
Learning Lie Group Symmetry Transformations with Neural NetworksCode0
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language ModelsCode0
Learning Neural Representations for Network Anomaly DetectionCode0
Choosing the Number of Topics in LDA Models -- A Monte Carlo Comparison of Selection CriteriaCode0
Deep Active Learning with Adaptive AcquisitionCode0
Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary ObjectivesCode0
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