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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 1701–1750 of 2050 papers

TitleStatusHype
Learning Sparse Neural Networks through L_0 RegularizationCode0
Episodic memory for continual model learning—0
Regularized Modal Regression with Applications in Cognitive Impairment Prediction—0
Scalable Model Selection for Belief Networks—0
Prior and Likelihood Choices for Bayesian Matrix Factorisation on Small Datasets—0
Nonparametric Independence Screening via Favored Smoothing Bandwidth—0
Population Based Training of Neural NetworksCode1
Parameter Reference Loss for Unsupervised Domain Adaptation—0
Distributed Bayesian Piecewise Sparse Linear Models—0
Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free RegularizationCode0
Network Model Selection Using Task-Focused Minimum Description Length—0
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data—0
Adaptive multi-penalty regularization based on a generalized Lasso pathCode0
Nonsparse learning with latent variables—0
Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics—0
DAGGER: A sequential algorithm for FDR control on DAGsCode0
The Merging Path Plot: adaptive fusing of k-groups with likelihood-based model selectionCode0
Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning—0
Modeling Sheep pox Disease from the 1994-1998 Epidemic in Evros Prefecture, Greece—0
MSM lag time cannot be used for variational model selection—0
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
Evasion Attacks against Machine Learning at Test Time—0
Network Model Selection for Task-Focused Attributed Network Inference—0
Fixed effects testing in high-dimensional linear mixed models—0
When Is the First Spurious Variable Selected by Sequential Regression Procedures?—0
Automatic Selection of t-SNE Perplexity—0
Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications—0
Neural Vector Spaces for Unsupervised Information RetrievalCode0
Data-driven Advice for Applying Machine Learning to Bioinformatics ProblemsCode0
A network approach to topic modelsCode1
Nonparametric weighted stochastic block models—0
Sparse model selection via integral terms—0
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle—0
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption—0
LIMSI@CoNLL'17: UD Shared Task—0
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering—0
UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise FeaturesCode0
Probabilistic models of individual and collective animal behavior—0
Familia: An Open-Source Toolkit for Industrial Topic ModelingCode0
Consistent Nonparametric Different-Feature Selection via the Sparsest k-Subgraph Problem—0
Boosted Zero-Shot Learning with Semantic Correlation Regularization—0
Graphical posterior predictive classifier: Bayesian model averaging with particle GibbsCode0
Variational approach for learning Markov processes from time series data—0
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix FactorisationCode0
Model Selection for Anomaly Detection—0
CHARDA: Causal Hybrid Automata Recovery via Dynamic AnalysisCode0
Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell TimeCode0
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—0
High-dimensional classification by sparse logistic regressionCode0
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation—0
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