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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 1501–1550 of 2050 papers

TitleStatusHype
Stability selection enables robust learning of partial differential equations from limited noisy dataCode0
Improving classification performance by feature space transformations and model selection—0
Sparsely Activated NetworksCode0
On the Evaluation of Conditional GANsCode0
Change point detection for graphical models in the presence of missing valuesCode0
PreCall: A Visual Interface for Threshold Optimization in ML Model Selection—0
Competing Models—0
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction—0
Multiple Testing and Variable Selection along the path of the Least Angle RegressionCode0
MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data—0
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithmsCode0
Deep Active Learning with Adaptive AcquisitionCode0
Inferring Latent dimension of Linear Dynamical System with Minimum Description Length—0
Model selection for high-dimensional linear regression with dependent observations—0
The PRIMPing Routine -- Tiling through Proximal Alternating Linearized Minimization—0
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
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage—0
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter—0
INFaaS: A Model-less and Managed Inference Serving SystemCode0
Lifelong Bayesian Optimization—0
Unsupervised Model Selection for Variational Disentangled Representation Learning—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
Gmail Smart Compose: Real-Time Assisted Writing—0
Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression—0
Information criteria for non-normalized models—0
Automatic Model Selection for Neural Networks—0
Decision Making with Machine Learning and ROC Curves—0
Disentangling Factors of Variation Using Few Labels—0
Post-Selection Inference in Three-Dimensional Panel Data—0
S^2-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning—0
On Learning to Prove—0
Bayesian leave-one-out cross-validation for large data—0
A deep learning based solution for construction equipment detection: from development to deployment—0
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