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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 1051–1075 of 2050 papers

TitleStatusHype
Belief propagation for permutations, rankings, and partial orders—0
The Infinite Contextual Graph Markov Model—0
Gradient-based Hyperparameter Optimization without Validation Data for Learning fom Limited Labels—0
On the Uncomputability of Partition Functions in Energy-Based Sequence Models—0
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data—0
Probability Distribution on Full Rooted Trees—0
Towards trustworthy explanations with gradient-based attribution methods—0
The supremum principle selects simple, transferable models—0
Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language—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
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
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