SOTAVerified

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 76–100 of 2050 papers

TitleStatusHype
An Information-theoretic Approach to Distribution ShiftsCode1
An information criterion for automatic gradient tree boostingCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark StudyCode1
A comparison of methods for model selection when estimating individual treatment effectsCode1
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical CareCode1
AQuA: A Benchmarking Tool for Label Quality AssessmentCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster ManagementCode1
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
Adversarial Branch Architecture Search for Unsupervised Domain AdaptationCode1
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label LearningCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
Assumption-lean inference for generalised linear model parametersCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation TasksCode1
Empirical evaluation of scoring functions for Bayesian network model selectionCode1
A network approach to topic modelsCode1
Automated Machine Learning in InsuranceCode1
BERTScore: Evaluating Text Generation with BERTCode1
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing dataCode1
Evaluating Weakly Supervised Object Localization Methods RightCode1
Automatic Model Selection with Large Language Models for ReasoningCode1
CNN Model & Tuning for Global Road Damage DetectionCode1
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