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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 301–350 of 2050 papers

TitleStatusHype
Smart Multi-Modal Search: Contextual Sparse and Dense Embedding Integration in Adobe Express—0
Automated Machine Learning in InsuranceCode1
LalaEval: A Holistic Human Evaluation Framework for Domain-Specific Large Language Models—0
HBIC: A Biclustering Algorithm for Heterogeneous DatasetsCode0
Multiple testing for signal-agnostic searches of new physics with machine learningCode0
Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models—0
Hologram Reasoning for Solving Algebra Problems with Geometry DiagramsCode1
Area under the ROC Curve has the Most Consistent Evaluation for Binary Classification—0
Identifying Technical Debt and Its Types Across Diverse Software Projects Issues—0
LEVIS: Large Exact Verifiable Input Spaces for Neural Networks—0
Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discoveryCode0
eGAD! double descent is explained by Generalized Aliasing Decomposition—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary ObjectivesCode0
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
Winners with Confidence: Discrete Argmin Inference with an Application to Model Selection—0
The Mismeasure of Man and Models: Evaluating Allocational Harms in Large Language Models—0
Within-vector viral dynamics challenges how to model the extrinsic incubation period for major arboviruses: dengue, Zika, and chikungunya—0
A Dirichlet stochastic block model for composition-weighted networks—0
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition—0
FiCo-ITR: bridging fine-grained and coarse-grained image-text retrieval for comparative performance analysisCode0
AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video AnalyticsCode0
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
ClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction TasksCode1
Closing the gap between open-source and commercial large language models for medical evidence summarization—0
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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