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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 1126–1150 of 2050 papers

TitleStatusHype
Towards trustworthy explanations with gradient-based attribution methods—0
Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language—0
The supremum principle selects simple, transferable models—0
Automatic Componentwise Boosting: An Interpretable AutoML System—0
Estimation of Local Average Treatment Effect by Data Combination—0
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits—0
Learning the hypotheses space from data through a U-curve algorithm—0
Adaptive variational Bayes: Optimality, computation and applications—0
Functional additive models on manifolds of planar shapes and forms—0
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT—0
Optimization Networks for Integrated Machine Learning—0
Markov Switching Model for Driver Behavior Prediction: Use cases on Smartphones—0
Consistent Relative Confidence and Label-Free Model Selection for Convolutional Neural Networks—0
Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale—0
Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT—0
4-D Epanechnikov Mixture Regression in Light Field Image Compression—0
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
Inferring bias and uncertainty in camera calibration—0
Order Book Queue Hawkes-Markovian Modeling—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
Model-Parallel Model Selection for Deep Learning Systems—0
Model Selection for Generic Reinforcement Learning—0
Fast approximations of the Jeffreys divergence between univariate Gaussian mixture models via exponential polynomial densities—0
Gaussian Process Subspace Regression for Model ReductionCode0
Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential EquationsCode0
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