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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 326–350 of 2050 papers

TitleStatusHype
Superior Scoring Rules for Probabilistic Evaluation of Single-Label Multi-Class Classification TasksCode1
Navigating Uncertainty in Medical Image Segmentation—0
Patched RTC: evaluating LLMs for diverse software development tasksCode0
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and FairnessCode0
Zero-Shot Embeddings Inform Learning and Forgetting with Vision-Language Encoders—0
Modeling flexible behavior with remapping-based hippocampal sequence learning—0
Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes—0
Is F_1 Score Suboptimal for Cybersecurity Models? Introducing C_score, a Cost-Aware Alternative for Model Assessment—0
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity ModelsCode0
Realistic Evaluation of Test-Time Adaptation Algorithms: Unsupervised Hyperparameter Selection—0
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity—0
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence—0
Subject-driven Text-to-Image Generation via Preference-based Reinforcement LearningCode0
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse ModalitiesCode1
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph BenchmarkCode1
Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPsCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation SystemsCode0
On Leakage of Code Generation Evaluation Datasets—0
Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function IdentificationsCode0
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
Comparative Analysis of LSTM Neural Networks and Traditional Machine Learning Models for Predicting Diabetes Patient Readmission—0
TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning BenchmarksCode4
Zero-shot prompt-based classification: topic labeling in times of foundation models in German Tweets—0
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