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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 1351–1375 of 2050 papers

TitleStatusHype
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
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification—0
Hidden Markov Models Applied To Intraday Momentum Trading With Side Information—0
Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining—0
Regret Balancing for Bandit and RL Model Selection—0
Speedy Performance Estimation for Neural Architecture SearchCode0
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective—0
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach—0
Virtual Reference Feedback Tuning with data-driven reference model selection—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
DGSAC: Density Guided Sampling and Consensus—0
Unsupervised Discretization by Two-dimensional MDL-based HistogramCode0
Variational Inference and Learning of Piecewise-linear Dynamical Systems—0
Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction—0
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
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