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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
Optimality in importance sampling: a gentle survey—0
The Value of Information in Human-AI Decision-making—0
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
Mixture of neural operator experts for learning boundary conditions and model selection—0
Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and ClassificationCode1
Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor AttacksCode0
Vision-Language Model Selection and Reuse for Downstream Adaptation—0
A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions—0
Scaling Inference-Efficient Language Models—0
Data-Informed Model Complexity Metric for Optimizing Symbolic Regression Models—0
Dynamics of Transient Structure in In-Context Linear Regression Transformers—0
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM PerformanceCode0
Quantifying Uncertainty and Variability in Machine Learning: Confidence Intervals for Quantiles in Performance Metric Distributions—0
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST—0
Time Series Embedding Methods for Classification Tasks: A ReviewCode1
A Bayesian Modelling Framework with Model Comparison for Epidemics with Super-SpreadingCode0
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands—0
Statistical Inference for Sequential Feature Selection after Domain AdaptationCode0
Principled model selection for stochastic dynamics—0
Utilizing AI Language Models to Identify Prognostic Factors for Coronary Artery Disease: A Study in Mashhad Residents—0
Empowering Agricultural Insights: RiceLeafBD - A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique—0
On the use of Statistical Learning Theory for model selection in Structural Health Monitoring—0
Fast sampling and model selection for Bayesian mixture models—0
An Investigation into Seasonal Variations in Energy Forecasting for Student Residences—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context—0
Unsupervised detection and fitness estimation of emerging SARS-CoV-2 variants. Application to wastewater samples (ANRS0160)Code0
Private Selection with Heterogeneous Sensitivities—0
An Instrumental Variables Approach to Testing Firm Conduct—0
How to Select Pre-Trained Code Models for Reuse? A Learning Perspective—0
MODfinity: Unsupervised Domain Adaptation with Multimodal Information Flow Intertwining—0
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks—0
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework—0
Adversarial Negotiation Dynamics in Generative Language Models—0
Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing dataCode1
Recommending Pre-Trained Models for IoT Devices—0
Structure Learning in Gaussian Graphical Models from Glauber Dynamics—0
Exploring Dynamic Novel View Synthesis Technologies for Cinematography—0
Towards Unsupervised Model Selection for Domain Adaptive Object DetectionCode1
Know2Vec: A Black-Box Proxy for Neural Network RetrievalCode0
YOLOv11 Optimization for Efficient Resource UtilizationCode0
From Human Annotation to LLMs: SILICON Annotation Workflow for Management Research—0
Quantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities—0
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric PerspectiveCode0
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Foundational Large Language Models for Materials ResearchCode2
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection—0
NLP-ADBench: NLP Anomaly Detection BenchmarkCode1
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