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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 176–200 of 2050 papers

TitleStatusHype
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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