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 301–310 of 2050 papers

TitleStatusHype
MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model SelectionCode0
Evaluating Large Language Models as Generative User Simulators for Conversational RecommendationCode0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
Fast Cross-Validation via Sequential TestingCode0
GLEMOS: Benchmark for Instantaneous Graph Learning Model SelectionCode0
ARDA: Automatic Relational Data Augmentation for Machine LearningCode0
Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free RegularizationCode0
A Convex Framework for Confounding Robust InferenceCode0
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature ExtractorsCode0
A general technique for the estimation of farm animal body part weights from CT scans and its applications in a rabbit breeding programCode0
Show:102550
← PrevPage 31 of 205Next →

No leaderboard results yet.