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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 1201–1250 of 2050 papers

TitleStatusHype
Non-asymptotic model selection in block-diagonal mixture of polynomial experts models—0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity—0
Scalable Marginal Likelihood Estimation for Model Selection in Deep LearningCode0
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts modelsCode0
A Federated Learning Framework for Non-Intrusive Load Monitoring—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—0
Domain adaptation in practice: Lessons from a real-world information extraction pipeline—0
Model Selection's Disparate Impact in Real-World Deep Learning Applications—0
Model Selection for Time Series Forecasting: Empirical Analysis of Different EstimatorsCode0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation—0
A Powerful Subvector Anderson Rubin Test in Linear Instrumental Variables Regression with Conditional Heteroskedasticity—0
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source LayoutCode0
The Shape of Learning Curves: a ReviewCode0
Deep Time Series Models for Scarce Data—0
Learning Word-Level Confidence For Subword End-to-End ASR—0
Reframing Neural Networks: Deep Structure in Overcomplete Representations—0
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies—0
Model Complexity of Deep Learning: A Survey—0
Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases—0
General Bayesian time-varying parameter VARs for predicting government bond yields—0
Partially Hidden Markov Chain Linear Autoregressive model: inference and forecastingCode0
AutoAI-TS: AutoAI for Time Series Forecasting—0
Two Sides of Meta-Learning Evaluation: In vs. Out of DistributionCode0
Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies—0
Bridging factor and sparse models—0
Joint Continuous and Discrete Model Selection via Submodularity—0
Geostatistical Learning: Challenges and OpportunitiesCode0
Parsimonious Modelling for Estimating Hospital Cooling Demand to Improve Energy Efficiency—0
Pareto Optimal Model Selection in Linear Bandits—0
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge—0
Derivative-Free Reinforcement Learning: A Review—0
Ising Model Selection Using _1-Regularized Linear Regression: A Statistical Mechanics Analysis—0
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems—0
Vine copula mixture models and clustering for non-Gaussian dataCode0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—0
Bayesian data-driven discovery of partial differential equations with variable coefficients—0
Ranking vs. Classifying: Measuring Knowledge Base Completion QualityCode0
Sequential prediction under log-loss and misspecification—0
A linearized framework and a new benchmark for model selection for fine-tuning—0
AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models—0
Choice modelling in the age of machine learning - discussion paper—0
Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: The ALQR Approach—0
A Characterization for Optimal Bundling of Products with Non-Additive Values—0
Supervised Momentum Contrastive Learning for Few-Shot Classification—0
Online and Scalable Model Selection with Multi-Armed Bandits—0
How do some Bayesian Network machine learned graphs compare to causal knowledge?—0
Hierarchical Variational Auto-Encoding for Unsupervised Domain Generalization—0
Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection—0
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