arXiv research

Multi-headed ensembles boost model performance with faster training.

Ensemble learning is a methodology that integrates multiple DNN learners for improving prediction performance of individual learners. Diversity is greater when the errors of the ensemble prediction is more uniformly distributed. Greater diversity is highly correlated with the increase in ensemble accuracy. Another attr…

Transforms ensemble predictions to maintain interpretability.

Overparameterized ensembles don't offer generalization benefits over single large models.

New method creates diverse neural ensembles for better uncertainty estimation and robustness.

New method certifies joint adversarial robustness of model ensembles.

Embedded ensembles improve neural network performance efficiently.

New hyperparameter ensembles boost neural network performance and uncertainty.

ECV method optimizes ensemble parameters for randomized ensembles.

The paper explores dynamic ensembles for multi-step forecasting.

Bayesian deep ensembles improve prediction accuracy in various settings.

RCAM-based ensemble combines binary classifiers using similarity and vote scheme.

Heterogeneous ensembles built from the predictions of a wide variety and large number of diverse base predictors represent a potent approach to building predictive models for problems where the ideal base/individual predictor may not be obvious. Ensemble selection is an especially promising approach here, not only for …

High-capacity neural network ensembles often benefit more from high-capacity models than from increased diversity.

New method reduces ensemble size for linear bandits, achieving near optimal regret.

Jointly tuning ensemble models improves performance and uncertainty calibration.

Proposes a new method for deep ensembles that improves accuracy and calibration.

Paper analyzes uncertainty metrics in ensemble learning for healthcare AI.

Self-paced ensemble learning improves audio classification models.

Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep learning ensembles have provided state-of-the-art results in uncertainty estimation. In this work, we focus on in-domain uncertainty for im…

We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to spheres rather than distance to instances. The randomised nature of RSC makes it ideal for use in en…

A fast method estimates stability of ensemble feature selectors.

Ensembles of neural networks have been shown to give better performance than single networks, both in terms of predictions and uncertainty estimation. Additionally, ensembles allow the uncertainty to be decomposed into aleatoric (data) and epistemic (model) components, giving a more complete picture of the predictive u…

Ensemble++ uses shared-factor ensembles to scale Thompson Sampling for linear and nonlinear bandits.

Ensemble unsupervised anomaly detection using IRT for hidden ground truth.

New method improves ensemble quality by exploring the pre-train basin more effectively.

Efficient neural network ensembles improve image classification reliability and uncertainty quantification.

Ensembles of deep neural networks significantly improve generalization accuracy. However, training neural network ensembles requires a large amount of computational resources and time. State-of-the-art approaches either train all networks from scratch leading to prohibitive training cost that allows only very small ens…

Paper analyzes ensemble Kalman updates for effective dimension and localization.

Ensembling DNNs improves minority group performance, leading to fairness.

Repulsive deep ensembles improve diversity and Bayesian inference.

This research tackles uncertainty in gradient boosting models using ensemble methods.

Fibonacci Ensembles use Fibonacci weights to improve ensemble learning, inspired by natural growth patterns.

Ensembling improves performance when classifiers disagree more than average.

Ensembles of models often yield improvements in system performance. These ensemble approaches have also been empirically shown to yield robust measures of uncertainty, and are capable of distinguishing between different \emph{forms} of uncertainty. However, ensembles come at a computational and memory cost which may be…

Ens-CGP synthesizes ensemble-based inference with Gaussian processes.

Ensembles improve classifier performance by reducing bias, not variance.

Ensembles of models have been empirically shown to improve predictive performance and to yield robust measures of uncertainty. However, they are expensive in computation and memory. Therefore, recent research has focused on distilling ensembles into a single compact model, reducing the computational and memory burden o…

Ensemble models refer to methods that combine a typically large number of classifiers into a compound prediction. The output of an ensemble method is the result of fitting a base-learning algorithm to a given data set, and obtaining diverse answers by reweighting the observations or by resampling them using a given pro…

Ensemble of diverse CNNs detects and mitigates adversarial attacks.

Auto-Ensemble automates deep learning model ensembling with adaptive learning rate scheduling.

Tree ensembles, such as random forest and boosted trees, are renowned for their high prediction performance, whereas their interpretability is critically limited. In this paper, we propose a post processing method that improves the model interpretability of tree ensembles. After learning a complex tree ensembles in a s…

Bayesian Quadrature improves ensembling for neural networks with dispersed likelihood peaks.

Improved regret bound for linear ensemble sampling.

Ensemble methods get better with more models if loss function is convex.

Ensembles of random-feature models can't outperform a single large model.

Clustering ensemble, or consensus clustering, has emerged as a powerful tool for improving both the robustness and the stability of results from individual clustering methods. Weighted clustering ensemble arises naturally from clustering ensemble. One of the arguments for weighted clustering ensemble is that elements (…

In this article, we propose a novel ensemble technique with a multi-scheme weighting based on a technique called coopetitive soft gating. This technique combines both, ensemble member competition and cooperation, in order to maximize the overall forecasting accuracy of the ensemble. The proposed algorithm combines the …