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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 651–700 of 2050 papers

TitleStatusHype
LEAD: Exploring Logit Space Evolution for Model Selection—0
Application of Machine Learning in Stock Market Forecasting: A Case Study of Disney Stock—0
Inconsistency of cross-validation for structure learning in Gaussian graphical models—0
Model Selection for Inverse Reinforcement Learning via Structural Risk Minimization—0
Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selection—0
RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems—0
Probabilistic Modeling for Sequences of Sets in Continuous-TimeCode0
Learning of networked spreading models from noisy and incomplete data—0
AutoXPCR: Automated Multi-Objective Model Selection for Time Series ForecastingCode0
Bayesian Model Selection via Mean-Field Variational Approximation—0
Random Models for Fuzzy Clustering Similarity Measures—0
Efficient speech detection in environmental audio using acoustic recognition and knowledge distillation—0
Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue—0
Predictive variational autoencoder for learning robust representations of time-series data—0
Evaluating the Utility of Model Explanations for Model Development—0
Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey—0
Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformers—0
Deep Bayes Factors—0
Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework—0
Towards Measuring Representational Similarity of Large Language ModelsCode0
Risk-Controlling Model Selection via Guided Bayesian Optimization—0
Approximation of Intractable Likelihood Functions in Systems Biology via Normalizing Flows—0
How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model RankingCode0
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature ExtractorsCode0
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification—0
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection—0
Task-Distributionally Robust Data-Free Meta-Learning—0
Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects—0
Improved identification accuracy in equation learning via comprehensive R^2-elimination and Bayesian model selectionCode0
GPT in Data Science: A Practical Exploration of Model Selection—0
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework—0
Supervised structure learning—0
How False Data Affects Machine Learning Models in Electrochemistry?Code0
To Translate or Not to Translate: A Systematic Investigation of Translation-Based Cross-Lingual Transfer to Low-Resource Languages—0
Comparison of model selection techniques for seafloor scattering statistics—0
Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count DataCode0
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
Unsupervised Video Summarization via Iterative Training and Simplified GANCode0
Saturn: Efficient Multi-Large-Model Deep Learning—0
Changing the Kernel During Training Leads to Double Descent in Kernel RegressionCode0
An energy-based comparative analysis of common approaches to text classification in the Legal domain—0
Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias—0
Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models—0
NoMoPy: Noise Modeling in Python—0
Optimizing accuracy and diversity: a multi-task approach to forecast combinations—0
Evaluating LLP Methods: Challenges and ApproachesCode0
Approximate Leave-one-out Cross Validation for Regression with _1 Regularizers (extended version)—0
Causal Q-Aggregation for CATE Model Selection—0
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive BenchmarkCode0
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