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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 26–50 of 2050 papers

TitleStatusHype
scikit-fda: A Python Package for Functional Data AnalysisCode2
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning PerspectiveCode2
DeepDPM: Deep Clustering With an Unknown Number of ClustersCode2
Tuning the Right Foundation Models is What you Need for Partial Label LearningCode1
DeSocial: Blockchain-based Decentralized Social NetworksCode1
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster ManagementCode1
LENSLLM: Unveiling Fine-Tuning Dynamics for LLM SelectionCode1
Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and ClassificationCode1
Time Series Embedding Methods for Classification Tasks: A ReviewCode1
Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing dataCode1
Towards Unsupervised Model Selection for Domain Adaptive Object DetectionCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
NLP-ADBench: NLP Anomaly Detection BenchmarkCode1
Evaluating Language Models as Synthetic Data GeneratorsCode1
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
Noether's razor: Learning Conserved QuantitiesCode1
SePPO: Semi-Policy Preference Optimization for Diffusion AlignmentCode1
Triple equivalence for the emergence of biological intelligenceCode1
Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion DetectionCode1
Automated Machine Learning in InsuranceCode1
Hologram Reasoning for Solving Algebra Problems with Geometry DiagramsCode1
ClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction TasksCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
Superior Scoring Rules for Probabilistic Evaluation of Single-Label Multi-Class Classification TasksCode1
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse ModalitiesCode1
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