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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 651–675 of 2050 papers

TitleStatusHype
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
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
Dual-stage optimizer for systematic overestimation adjustment applied to multi-objective genetic algorithms for biomarker selection—0
Model Selection for Inverse Reinforcement Learning via Structural Risk Minimization—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
How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model RankingCode0
Approximation of Intractable Likelihood Functions in Systems Biology via Normalizing Flows—0
Risk-Controlling Model Selection via Guided Bayesian Optimization—0
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature ExtractorsCode0
A Review of Cross-Sectional Matrix Exponential Spatial Models—0
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