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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 751–800 of 2050 papers

TitleStatusHype
Tryage: Real-time, intelligent Routing of User Prompts to Large Language Models—0
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE DiscoveryCode0
Open, Closed, or Small Language Models for Text Classification?—0
No Regularization is Needed: An Efficient and Effective Model for Incomplete Label Distribution Learning—0
A coupled-mechanisms modelling framework for neurodegeneration—0
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance—0
Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review—0
A Review of Change of Variable Formulas for Generative Modeling—0
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities—0
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation—0
Predictive Modeling through Hyper-Bayesian Optimization—0
A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI—0
An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading—0
Learning Disentangled Discrete RepresentationsCode0
Rational kernel-based interpolation for complex-valued frequency response functions—0
Consistent model selection in the spiked Wigner model via AIC-type criteria—0
Anytime Model Selection in Linear BanditsCode0
Adaptive debiased machine learning using data-driven model selection techniques—0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Towards a performance analysis on pre-trained Visual Question Answering models for autonomous drivingCode0
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and CalibrationCode0
The Interpolating Information Criterion for Overparameterized Models—0
DataAssist: A Machine Learning Approach to Data Cleaning and Preparation—0
Sparsified Simultaneous Confidence Intervals for High-Dimensional Linear Models—0
Risk Controlled Image RetrievalCode0
DSV: An Alignment Validation Loss for Self-supervised Outlier Model SelectionCode0
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks—0
Online Laplace Model Selection Revisited—0
GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts—0
Bayesian taut splines for estimating the number of modes—0
Action-State Dependent Dynamic Model Selection—0
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?—0
Learning Lie Group Symmetry Transformations with Neural NetworksCode0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neuronsCode0
Confidence-based Ensembles of End-to-End Speech Recognition Models—0
Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning—0
Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications—0
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts—0
Frame Fusion with Vehicle Motion Prediction for 3D Object Detection—0
Human Limits in Machine Learning: Prediction of Plant Phenotypes Using Soil Microbiome DataCode0
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization—0
Sliding Window Neural Generated Tracking Based on Measurement Model—0
Two-level histograms for dealing with outliers and heavy tail distributions—0
Gibbs-Based Information Criteria and the Over-Parameterized Regime—0
Stochastic Marginal Likelihood Gradients using Neural Tangent KernelsCode0
Data-Driven Online Model Selection With Regret Guarantees—0
Bivariate Causal Discovery using Bayesian Model SelectionCode0
Structured model selection via _1-_2 optimizationCode0
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint AveragingCode0
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