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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 51–75 of 2050 papers

TitleStatusHype
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical CareCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
DeSocial: Blockchain-based Decentralized Social NetworksCode1
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label LearningCode1
An information criterion for automatic gradient tree boostingCode1
A stacked DCNN to predict the RUL of a turbofan engineCode1
Assumption-lean inference for generalised linear model parametersCode1
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Empirical evaluation of scoring functions for Bayesian network model selectionCode1
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
Change is Hard: A Closer Look at Subpopulation ShiftCode1
clusterBMA: Bayesian model averaging for clusteringCode1
A comparison of methods for model selection when estimating individual treatment effectsCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
An Information-theoretic Approach to Distribution ShiftsCode1
CNN Model & Tuning for Global Road Damage DetectionCode1
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular dataCode1
Can We Characterize Tasks Without Labels or Features?Code1
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration ErrorCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion ModelsCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
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