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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 1176–1200 of 2050 papers

TitleStatusHype
Gibbs-Based Information Criteria and the Over-Parameterized Regime—0
Sharp Convergence Rates for Forward Regression in High-Dimensional Sparse Linear Models—0
Fast Partition-Based Cross-Validation With Centering and Scaling for X^TX and X^TY—0
Short-term prediction of photovoltaic power generation using Gaussian process regression—0
Short-time asymptotics for the implied volatility skew under a stochastic volatility model with L\'evy jumps—0
Sig-SDEs model for quantitative finance—0
Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study—0
Simultaneous Clustering and Model Selection for Tensor Affinities—0
Simultaneous Identification of Sparse Structures and Communities in Heterogeneous Graphical Models—0
Simultaneous Localization and Layout Model Selection in Manhattan Worlds—0
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning—0
Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC—0
Simultaneous Subspace Clustering and Cluster Number Estimating based on Triplet Relationship—0
Sliding Window Neural Generated Tracking Based on Measurement Model—0
Small Data, Big Decisions: Model Selection in the Small-Data Regime—0
Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare—0
Smart Multi-Modal Search: Contextual Sparse and Dense Embedding Integration in Adobe Express—0
Smooth Bandit Optimization: Generalization to Hölder Space—0
Solar Power Prediction Using Machine Learning—0
SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks—0
SPAgent: Adaptive Task Decomposition and Model Selection for General Video Generation and Editing—0
Sparse Estimation with Structured Dictionaries—0
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient—0
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?—0
Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection—0
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