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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 476–500 of 2050 papers

TitleStatusHype
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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