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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 476–500 of 2050 papers

TitleStatusHype
Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systems—0
Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models—0
LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot SystemsCode0
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized LearningCode0
MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications—0
Zero-shot Outlier Detection via Prior-data Fitted Networks: Model Selection Bygone!—0
Towards Safer Online Spaces: Simulating and Assessing Intervention Strategies for Eating Disorder Discussions—0
On the effectiveness of smartphone IMU sensors and Deep Learning in the detection of cardiorespiratory conditions—0
Smart Multi-Modal Search: Contextual Sparse and Dense Embedding Integration in Adobe Express—0
On the Effects of Modeling on the Sim-to-Real Transfer Gap in Twinning the POWDER Platform—0
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
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
A Dirichlet stochastic block model for composition-weighted networks—0
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