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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 101–125 of 2050 papers

TitleStatusHype
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian QuadratureCode1
UniASM: Binary Code Similarity Detection without Fine-tuningCode1
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular dataCode1
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter TuningCode1
XAI for transparent wind turbine power curve modelsCode1
Graph Anomaly Detection with Unsupervised GNNsCode1
PARAGEN : A Parallel Generation ToolkitCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
Unsupervised Model Selection for Time-series Anomaly DetectionCode1
Entropic Descent Archetypal Analysis for Blind Hyperspectral UnmixingCode1
A new family of Constitutive Artificial Neural Networks towards automated model discoveryCode1
clusterBMA: Bayesian model averaging for clusteringCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
HyperImpute: Generalized Iterative Imputation with Automatic Model SelectionCode1
Unsupervised Image Representation Learning with Deep Latent ParticlesCode1
Time Series Anomaly Detection via Reinforcement Learning-Based Model SelectionCode1
NICO++: Towards Better Benchmarking for Domain GeneralizationCode1
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
Monitored Distillation for Positive Congruent Depth CompletionCode1
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
Rich Feature Construction for the Optimization-Generalization DilemmaCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think AgainCode1
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification TasksCode1
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