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 201–225 of 2050 papers

TitleStatusHype
Modeling the Second Player in Distributionally Robust OptimizationCode1
Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical CareCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
DEPARA: Deep Attribution Graph for Deep Knowledge TransferabilityCode1
DeSocial: Blockchain-based Decentralized Social NetworksCode1
Assumption-lean inference for generalised linear model parametersCode1
SePPO: Semi-Policy Preference Optimization for Diffusion AlignmentCode1
A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engineCode1
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think AgainCode1
Tighter risk certificates for neural networksCode1
Adaptive Concentration of Regression Trees, with Application to Random ForestsCode0
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov modelsCode0
Evaluation of HTR models without Ground Truth MaterialCode0
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)Code0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
A multiple testing framework for diagnostic accuracy studies with co-primary endpointsCode0
Evaluating LLP Methods: Challenges and ApproachesCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
EPP: interpretable score of model predictive powerCode0
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic DatasetsCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Differentiable Model Selection for Ensemble LearningCode0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
Show:102550
← PrevPage 9 of 82Next →

No leaderboard results yet.