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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 451–500 of 2050 papers

TitleStatusHype
Understanding Model Selection For Learning In Strategic Environments—0
Label-Efficient Model Selection for Text Generation—0
Unsupervised Optimisation of GNNs for Node Clustering—0
Selective linear segmentation for detecting relevant parameter changes—0
Pretrained Generative Language Models as General Learning Frameworks for Sequence-Based Tasks—0
Tuning In: Analysis of Audio Classifier Performance in Clinical Settings with Limited Data—0
A Bandit Approach with Evolutionary Operators for Model Selection—0
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic DatasetsCode0
Best Practices for Text Annotation with Large Language Models—0
Absolute convergence and error thresholds in non-active adaptive sampling—0
Surfing the modeling of PoS taggers in low-resource scenarios—0
tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)Code0
Data-driven model selection within the matrix completion method for causal panel data models—0
Efficient and Effective Time-Series Forecasting with Spiking Neural NetworksCode2
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model LeaderboardsCode0
Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation—0
A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender SystemsCode0
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference—0
Is K-fold cross validation the best model selection method for Machine Learning?—0
MT-HCCAR: Multi-Task Deep Learning with Hierarchical Classification and Attention-based Regression for Cloud Property RetrievalCode0
Green Runner: A tool for efficient deep learning component selection—0
Fast Partition-Based Cross-Validation With Centering and Scaling for X^TX and X^TY—0
Towards Improved Variational Inference for Deep Bayesian Models—0
Budgeted Online Model Selection and Fine-Tuning via Federated Learning—0
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
Valid causal inference with unobserved confounding in high-dimensional settingsCode0
INTERS: Unlocking the Power of Large Language Models in Search with Instruction TuningCode3
An Axiomatic Approach to Model-Agnostic Concept Explanations—0
Arrival Time Prediction for Autonomous Shuttle Services in the Real World: Evidence from Five Cities—0
Experiment Planning with Function Approximation—0
Music Genre Classification: A Comparative Analysis of CNN and XGBoost Approaches with Mel-frequency cepstral coefficients and Mel Spectrograms—0
Understanding Short-Term Implied Volatility Dynamics: A Model-Independent Approach Beyond Stochastic Volatility—0
Comprehensive Exploration of Synthetic Data Generation: A Survey—0
U-Trustworthy Models.Reliability, Competence, and Confidence in Decision-Making—0
Few-shot Adaptation of Multi-modal Foundation Models: A Survey—0
Explainable Adaptive Tree-based Model Selection for Time Series Forecasting—0
Enhancing the Power of OOD Detection via Sample-Aware Model Selection—0
LEAD: Exploring Logit Space Evolution for Model Selection—0
Automated Model Selection for Tabular DataCode0
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models—0
Application of Machine Learning in Stock Market Forecasting: A Case Study of Disney Stock—0
GeoGalactica: A Scientific Large Language Model in GeoscienceCode1
Inconsistency of cross-validation for structure learning in Gaussian graphical models—0
RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems—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
Probabilistic Modeling for Sequences of Sets in Continuous-TimeCode0
Learning of networked spreading models from noisy and incomplete data—0
A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation TasksCode1
AutoXPCR: Automated Multi-Objective Model Selection for Time Series ForecastingCode0
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