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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 951–1000 of 2050 papers

TitleStatusHype
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion—0
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis—0
A study on the distribution of social biases in self-supervised learning visual models—0
Gaussian Process-based Spatial Reconstruction of Electromagnetic fields—0
Regularized Bilinear Discriminant Analysis for Multivariate Time Series Data—0
Exploratory Hidden Markov Factor Models for Longitudinal Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae—0
Ensemble Method for Estimating Individualized Treatment Effects—0
Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluation—0
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition—0
Efficient Distributed DNNs in the Mobile-edge-cloud Continuum—0
Bayesian Model Selection, the Marginal Likelihood, and GeneralizationCode1
Invariance Learning in Deep Neural Networks with Differentiable Laplace ApproximationsCode1
Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks—0
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants—0
Embarrassingly Simple Performance Prediction for Abductive Natural Language InferenceCode0
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
Multi-Objective Model Selection for Time Series Forecasting—0
Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold RegressionCode0
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomesCode0
Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments—0
Fitting Sparse Markov Models to Categorical Time Series Using Regularization—0
Loss-guided Stability Selection—0
Dependence model assessment and selection with DecoupleNets—0
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing dataCode1
Discovering Distribution Shifts using Latent Space RepresentationsCode0
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series—0
Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing—0
JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion—0
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study—0
Learning Curves for Decision Making in Supervised Machine Learning: A Survey—0
On Evaluation Metrics for Graph Generative ModelsCode1
The Time-Varying Multivariate Autoregressive Index Model—0
Evaluation of HTR models without Ground Truth MaterialCode0
One Step Is Enough for Few-Shot Cross-Lingual Transfer: Co-Training with Gradient Optimization—0
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset—0
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics—0
Problem-dependent attention and effort in neural networks with applications to image resolution and model selection—0
Self-directed Machine Learning—0
Have I done enough planning or should I plan more?Code0
Deep Learning and Linear Programming for Automated Ensemble Forecasting and InterpretationCode0
Propagation Regularizer for Semi-Supervised Learning With Extremely Scarce Labeled Samples—0
A general technique for the estimation of farm animal body part weights from CT scans and its applications in a rabbit breeding programCode0
Optimal Model Averaging of Support Vector Machines in Diverging Model Spaces—0
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations—0
Model Selection in Batch Policy Optimization—0
Latent Time Neural Ordinary Differential Equations—0
Optimal and instance-dependent guarantees for Markovian linear stochastic approximation—0
Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFNα therapy against Myeloproliferative Neoplasms—0
Optimal Latent Space Forecasting for Large Collections of Short Time Series Using Temporal Matrix Factorization—0
Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market—0
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