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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 801–825 of 2050 papers

TitleStatusHype
UniASM: Binary Code Similarity Detection without Fine-tuningCode1
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian QuadratureCode1
Estimation of Heterogeneous Treatment Effects Using a Conditional Moment Based Approach—0
TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection—0
Which is the best model for my data?—0
Improving Group Lasso for high-dimensional categorical data—0
Post-Selection Confidence Bounds for Prediction PerformanceCode0
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter TuningCode1
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular dataCode1
XAI for transparent wind turbine power curve modelsCode1
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal TransportCode0
Scalable Bayesian Transformed Gaussian Processes—0
Graph Anomaly Detection with Unsupervised GNNsCode1
Measures of Information Reflect Memorization Patterns—0
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data—0
LEATHER: A Framework for Learning to Generate Human-like Text in DialogueCode0
On the calibration of underrepresented classes in LiDAR-based semantic segmentation—0
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble LearningCode0
PARAGEN : A Parallel Generation ToolkitCode1
To tree or not to tree? Assessing the impact of smoothing the decision boundaries—0
Multi-View Independent Component Analysis with Shared and Individual Sources—0
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
MEDFAIR: Benchmarking Fairness for Medical ImagingCode0
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors—0
Detection and Evaluation of Clusters within Sequential Data—0
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