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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
Hydra: A System for Large Multi-Model Deep LearningCode1
An Information-theoretic Approach to Distribution ShiftsCode1
BayesOpt Adversarial AttackCode1
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models CascadeCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM EvaluationCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
In Search of Lost Domain GeneralizationCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
Laplace Redux -- Effortless Bayesian Deep LearningCode1
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
A comparison of methods for model selection when estimating individual treatment effectsCode1
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain GeneralizationCode1
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR ImagesCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration ErrorCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
Can We Characterize Tasks Without Labels or Features?Code1
cegpy: Modelling with Chain Event Graphs in PythonCode1
Machine Learning for Dynamic Resource Allocation in Network Function VirtualizationCode1
A Survey and Implementation of Performance Metrics for Self-Organized MapsCode1
Change is Hard: A Closer Look at Subpopulation ShiftCode1
Assumption-lean inference for generalised linear model parametersCode1
clusterBMA: Bayesian model averaging for clusteringCode1
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label LearningCode1
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter TuningCode1
A stacked DCNN to predict the RUL of a turbofan engineCode1
NICO++: Towards Better Benchmarking for Domain GeneralizationCode1
CNN Model & Tuning for Global Road Damage DetectionCode1
Noether's razor: Learning Conserved QuantitiesCode1
Conditional Matrix Flows for Gaussian Graphical ModelsCode1
Online Active Model Selection for Pre-trained ClassifiersCode1
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
Counterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline EvaluationCode1
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
Entropic Descent Archetypal Analysis for Blind Hyperspectral UnmixingCode1
PARAGEN : A Parallel Generation ToolkitCode1
Extended Stochastic Block Models with Application to Criminal NetworksCode1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
Data thinning for convolution-closed distributionsCode1
RBFOpt: an open-source library for black-box optimization with costly function evaluationsCode1
Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence ModelsCode1
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet DatasetsCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Data Models for Dataset Drift Controls in Machine Learning With Optical ImagesCode1
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular dataCode1
ClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction TasksCode1
Quantifying & Modeling Multimodal Interactions: An Information Decomposition FrameworkCode1
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