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Ensemble Learning

433 papers

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Papers

Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles NIPS 2016
Swapout: Learning an ensemble of deep architectures NIPS 2016
Logistic Boosting Regression for Label Distribution Learning CVPR 2016
Sequence Student-Teacher Training of Deep Neural Networks INTERSPEECH 2016
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests JMLR 2016
mlr: Machine Learning in R JMLR 2016
DART: Dropouts meet Multiple Additive Regression Trees AISTATS 2015
Learning to Propose Objects CVPR 2015
Oriented Edge Forests for Boundary Detection CVPR 2015
A Randomized Ensemble Approach to Industrial CT Segmentation ICCV 2015
Learning Ensembles of Potential Functions for Structured Prediction With Latent Variables ICCV 2015
Mondrian Forests: Efficient Online Random Forests NIPS 2014
A Drifting-Games Analysis for Online Learning and Applications to Boosting NIPS 2014
Consistency of weighted majority votes NIPS 2014
Multi-Resolution Cascades for Multiclass Object Detection NIPS 2014
Learning with Pseudo-Ensembles NIPS 2014
Multi-Class Deep Boosting NIPS 2014
Diverse Randomized Agents Vote to Win NIPS 2014
Theoretical Analyses on Ensemble and Multiple Kernel Regressors ACML 2014
Open Problem: A (Missing) Boosting-type Convergence Result for AdaBoost.MH with Factorized Multi-class Classifiers COLT 2014
Efficient Boosted Exemplar-based Face Detection CVPR 2014
Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting ICML 2014
Deep Boosting ICML 2014
A Convergence Rate Analysis for LogitBoost, MART and Their Variant ICML 2014
Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? JMLR 2014