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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 326–350 of 2050 papers

TitleStatusHype
client2vec: Towards Systematic Baselines for Banking Applications—0
Combinatorially Generated Piecewise Activation Functions—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
Block-Term Tensor Decomposition Model Selection and Computation: The Bayesian Way—0
A Systematic Analysis of Base Model Choice for Reward Modeling—0
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study—0
A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models—0
A Practitioner's Guide to Automatic Kernel Search for Gaussian Processes in Battery Applications—0
Approximation of Intractable Likelihood Functions in Systems Biology via Normalizing Flows—0
A first econometric analysis of the CRIX family—0
Choice modelling in the age of machine learning - discussion paper—0
Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework—0
Approximate Leave-one-out Cross Validation for Regression with _1 Regularizers (extended version)—0
A Federated Learning Framework for Non-Intrusive Load Monitoring—0
Application of Machine Learning in Stock Market Forecasting: A Case Study of Disney Stock—0
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines—0
Causal Falling Rule Lists—0
A Powerful Subvector Anderson Rubin Test in Linear Instrumental Variables Regression with Conditional Heteroskedasticity—0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection—0
Adversarial Negotiation Dynamics in Generative Language Models—0
Causal Discovery in Hawkes Processes by Minimum Description Length—0
Causal Q-Aggregation for CATE Model Selection—0
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation—0
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach—0
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