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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 301–325 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
A Dirichlet stochastic block model for composition-weighted networks—0
Within-vector viral dynamics challenges how to model the extrinsic incubation period for major arboviruses: dengue, Zika, and chikungunya—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
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