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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 426–450 of 2050 papers

TitleStatusHype
Efficient Model Compression for Bayesian Neural Networks—0
MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees—0
Leveraging LLMs for MT in Crisis Scenarios: a blueprint for low-resource languages—0
Model-free Estimation of Latent Structure via Multiscale Nonparametric Maximum Likelihood—0
Power side-channel leakage localization through adversarial training of deep neural networksCode0
Bayesian Regression for Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns—0
Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows—0
Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational ObjectiveCode0
Towards Better Open-Ended Text Generation: A Multicriteria Evaluation FrameworkCode0
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness—0
Stabilizing black-box model selection with the inflated argmax—0
e-Values for Real-Time Residential Electricity Demand Forecast Model Selection—0
TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment—0
SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning—0
High-Fidelity Transfer of Functional Priors for Wide Bayesian Neural Networks by Learning ActivationsCode0
Towards Unsupervised Validation of Anomaly-Detection Models—0
ORSO: Accelerating Reward Design via Online Reward Selection and Policy OptimizationCode0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective LandscapesCode0
Transformers4NewsRec: A Transformer-based News Recommendation Framework—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
A Unified Approach to Routing and Cascading for LLMs—0
Impact of Missing Values in Machine Learning: A Comprehensive Analysis—0
Decision-Aware Predictive Model Selection for Workforce Allocation—0
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language ModelsCode0
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