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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 1576–1600 of 2050 papers

TitleStatusHype
On Quantum Ambiguity and Potential Exponential Computational Speed-Ups to Solving Dynamic Asset Pricing Models—0
On Sparsity and Overcompleteness in Image Models—0
On summarized validation curves and generalization—0
On the calibration of underrepresented classes in LiDAR-based semantic segmentation—0
On the complexity of logistic regression models—0
On the Complexity of Representation Learning in Contextual Linear Bandits—0
On the Computation and Applications of Large Dense Partial Correlation Networks—0
On the effectiveness of smartphone IMU sensors and Deep Learning in the detection of cardiorespiratory conditions—0
On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging—0
On the Effects of Modeling on the Sim-to-Real Transfer Gap in Twinning the POWDER Platform—0
On the Equivalence of Factorized Information Criterion Regularization and the Chinese Restaurant Process Prior—0
Don't Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings—0
On-the-fly learning of adaptive strategies with bandit algorithms—0
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes—0
Selection, Ensemble, and Adaptation: Advancing Multi-Source-Free Domain Adaptation via Architecture Zoo—0
On the Necessity of Collaboration for Online Model Selection with Decentralized Data—0
On the overestimation of widely applicable Bayesian information criterion—0
On the relative performance of some parametric and nonparametric estimators of option prices—0
On the Reliability of Clustering Stability in the Large Sample Regime—0
On the Role of Supervision in Unsupervised Constituency Parsing—0
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data—0
On the safe use of prior densities for Bayesian model selection—0
On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation—0
On the Uncomputability of Partition Functions in Energy-Based Sequence Models—0
On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms—0
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