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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 151–200 of 2050 papers

TitleStatusHype
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain GeneralizationCode1
An Information-theoretic Approach to Distribution ShiftsCode1
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models CascadeCode1
HyperImpute: Generalized Iterative Imputation with Automatic Model SelectionCode1
BERTScore: Evaluating Text Generation with BERTCode1
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive RegularizersCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
Interpretable multiclass classification by MDL-based rule listsCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
Entropic Descent Archetypal Analysis for Blind Hyperspectral UnmixingCode1
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR ImagesCode1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
A comparison of methods for model selection when estimating individual treatment effectsCode1
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration ErrorCode1
LENSLLM: Unveiling Fine-Tuning Dynamics for LLM SelectionCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
LOVM: Language-Only Vision Model SelectionCode1
Can We Characterize Tasks Without Labels or Features?Code1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
cegpy: Modelling with Chain Event Graphs in PythonCode1
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and ClassificationCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
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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