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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 1501–1525 of 2050 papers

TitleStatusHype
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANsCode1
A New Compensatory Genetic Algorithm-Based Method for Effective Compressed Multi-function Convolutional Neural Network Model Selection with Multi-Objective Optimization—0
Variational Resampling Based Assessment of Deep Neural Networks under Distribution ShiftCode0
Using anomaly detection to support classification of fast running (packaging) processes—0
Estimating Real Log Canonical Thresholds—0
Off-Policy Evaluation via Off-Policy Classification—0
Model selection for contextual banditsCode0
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal InferenceCode0
Towards Accurate Model Selection in Deep Unsupervised Domain AdaptationCode0
Predicting Global Variations in Outdoor PM2.5 Concentrations using Satellite Images and Deep Convolutional Neural Networks—0
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter—0
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage—0
INFaaS: A Model-less and Managed Inference Serving SystemCode0
Unsupervised Model Selection for Variational Disentangled Representation Learning—0
Lifelong Bayesian Optimization—0
Deep Generalized Method of Moments for Instrumental Variable AnalysisCode0
A Geometric Modeling of Occam's Razor in Deep Learning—0
Cold Case: The Lost MNIST DigitsCode0
Variational Inference for Sparse Gaussian Process Modulated Hawkes ProcessCode0
Model Validation Using Mutated Training Labels: An Exploratory Study—0
Distributionally Robust Formulation and Model Selection for the Graphical LassoCode0
Adaptive Model Selection Framework: An Application to Airline Pricing—0
Catastrophic forgetting: still a problem for DNNsCode0
Analysis of the AutoML Challenge Series 2015–2018—0
Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood MaximizationCode0
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