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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 176–200 of 2050 papers

TitleStatusHype
clusterBMA: Bayesian model averaging for clusteringCode1
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label LearningCode1
Assumption-lean inference for generalised linear model parametersCode1
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter TuningCode1
NICO++: Towards Better Benchmarking for Domain GeneralizationCode1
Noether's razor: Learning Conserved QuantitiesCode1
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
A stacked DCNN to predict the RUL of a turbofan engineCode1
On Pitfalls of Test-Time AdaptationCode1
A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation TasksCode1
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification TasksCode1
Eryn : A multi-purpose sampler for Bayesian inferenceCode1
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of PovertyCode1
Deep learning for dynamic graphs: models and benchmarksCode1
RBFOpt: an open-source library for black-box optimization with costly function evaluationsCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Rethinking Parameter Counting in Deep Models: Effective Dimensionality RevisitedCode1
Data Models for Dataset Drift Controls in Machine Learning With Optical ImagesCode1
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular dataCode1
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
Data thinning for convolution-closed distributionsCode1
Laplace Redux -- Effortless Bayesian Deep LearningCode1
QuaPy: A Python-Based Framework for QuantificationCode1
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