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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–250 of 2050 papers

TitleStatusHype
Superior Scoring Rules for Probabilistic Evaluation of Single-Label Multi-Class Classification TasksCode1
Interpretable multiclass classification by MDL-based rule listsCode1
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
Testing Conditional Independence in Supervised Learning AlgorithmsCode1
SePPO: Semi-Policy Preference Optimization for Diffusion AlignmentCode1
Assumption-lean inference for generalised linear model parametersCode1
A stacked DCNN to predict the RUL of a turbofan engineCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
Time Series Embedding Methods for Classification Tasks: A ReviewCode1
Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion DetectionCode1
Adaptive Concentration of Regression Trees, with Application to Random ForestsCode0
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov modelsCode0
FiCo-ITR: bridging fine-grained and coarse-grained image-text retrieval for comparative performance analysisCode0
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)Code0
FedScore: A privacy-preserving framework for federated scoring system developmentCode0
fETSmcs: Feature-based ETS model component selectionCode0
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithmsCode0
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
Familia: An Open-Source Toolkit for Industrial Topic ModelingCode0
Fast Cross-Validation via Sequential TestingCode0
Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLPCode0
Face Spoofing Detection using Deep LearningCode0
How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of DocumentsCode0
Fast Instrument Learning with Faster RatesCode0
Finding the Homology of Decision Boundaries with Active LearningCode0
Exploring Model Transferability through the Lens of Potential EnergyCode0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical TextsCode0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic DatasetsCode0
Evaluation of HTR models without Ground Truth MaterialCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
ATM: A distributed, collaborative, scalable system for automated machine learningCode0
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender SystemsCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
Exploring Design Choices for Building Language-Specific LLMsCode0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question AnsweringCode0
A Test of Relative Similarity For Model Selection in Generative ModelsCode0
Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence ModelingCode0
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-LearningCode0
Fairness and bias correction in machine learning for depression prediction: results from four study populationsCode0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
A multiple testing framework for diagnostic accuracy studies with co-primary endpointsCode0
Automated Model Selection for Tabular DataCode0
Optimal design of experiments to identify latent behavioral typesCode0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Automatic AI Model Selection for Wireless Systems: Online Learning via Digital TwinningCode0
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a ReviewCode0
Show:102550
← PrevPage 5 of 41Next →

No leaderboard results yet.