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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 1476–1500 of 2050 papers

TitleStatusHype
Convergence Rates of Variational Inference in Sparse Deep Learning—0
Paired-Consistency: An Example-Based Model-Agnostic Approach to Fairness Regularization in Machine Learning—0
Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces—0
Multi-view Deep Subspace Clustering NetworksCode0
On the Existence of Simpler Machine Learning Models—0
Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models—0
Learning Neural Representations for Network Anomaly DetectionCode0
Adaptive spline fitting with particle swarm optimizationCode0
Doubly robust off-policy evaluation with shrinkage—0
Least Angle Regression in Tangent Space and LASSO for Generalized Linear Models—0
Stability selection enables robust learning of partial differential equations from limited noisy dataCode0
Improving classification performance by feature space transformations and model selection—0
Sparsely Activated NetworksCode0
On the Evaluation of Conditional GANsCode0
Change point detection for graphical models in the presence of missing valuesCode0
PreCall: A Visual Interface for Threshold Optimization in ML Model Selection—0
Competing Models—0
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction—0
MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data—0
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithmsCode0
Multiple Testing and Variable Selection along the path of the Least Angle RegressionCode0
Deep Active Learning with Adaptive AcquisitionCode0
Inferring Latent dimension of Linear Dynamical System with Minimum Description Length—0
Model selection for high-dimensional linear regression with dependent observations—0
The PRIMPing Routine -- Tiling through Proximal Alternating Linearized Minimization—0
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