arXiv research

Paper proposes ensemble distillation for well-calibrated structured prediction.

This paper develops a new theory for ensemble learning beyond variance reduction.

Real world systems typically feature a variety of different dependency types and topologies that complicate model selection for probabilistic graphical models. We introduce the ensemble-of-forests model, a generalization of the ensemble-of-trees model. Our model enables structure learning of Markov random fields (MRF) …

A new method reduces memory usage for deep neural networks ensembles.

GEnBP combines EnKF and GaBP for efficient high-dimensional inference.

Proposes a new method to enhance neural learning by maximizing information gain.

Artificial neural networks have been successfully applied to a variety of machine learning tasks, including image recognition, semantic segmentation, and machine translation. However, few studies fully investigated ensembles of artificial neural networks. In this work, we investigated multiple widely used ensemble meth…

Transforms ensemble predictions to maintain interpretability.

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

Unified framework for portfolio optimization using multiple hypotheses.

Compact Gaussian model approximates deep ensemble predictions.

New method models aptamer libraries as Boltzmann-weighted graph ensembles for better affinity predictions.

New hyperparameter ensembles boost neural network performance and uncertainty.

Statistical estimates can often be improved by fusion of data from several different sources. One example is so-called ensemble methods which have been successfully applied in areas such as machine learning for classification and clustering. In this paper, we present an ensemble method to improve community detection by…

s-RBFN integrates multiple hypotheses for efficient and diverse prediction.

This paper studies recursive ensembles driven by Fibonacci updates, improving learning dynamics.

This paper generalizes an important result from the PAC-Bayesian literature for binary classification to the case of ensemble methods for structured outputs. We prove a generic version of the \Cbound, an upper bound over the risk of models expressed as a weighted majority vote that is based on the first and second stat…

This paper introduces TNTK to study infinite soft tree ensembles.

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 …

Logifold improves ensemble machine learning by identifying fuzzy domains.

Several real problems ranging from text classification to computational biology are characterized by hierarchical multi-label classification tasks. Most of the methods presented in literature focused on tree-structured taxonomies, but only few on taxonomies structured according to a Directed Acyclic Graph (DAG). In thi…

ECV method optimizes ensemble parameters for randomized ensembles.

Packed-Ensembles improve uncertainty estimation in constrained hardware.

Ensemble methods for supervised machine learning have become popular due to their ability to accurately predict class labels with groups of simple, lightweight "base learners." While ensembles offer computationally efficient models that have good predictive capability they tend to be large and offer little insight into…

We propose an ensemble clustering algorithm for graphs (ECG), which is based on the Louvain algorithm and the concept of consensus clustering. We validate our approach by replicating a recently published study comparing graph clustering algorithms over artificial networks, showing that ECG outperforms the leading algor…

We generalize the recently discovered relationship between JT gravity and double-scaled random matrix theory to the case that the boundary theory may have time-reversal symmetry and may have fermions with or without supersymmetry. The matching between variants of JT gravity and matrix ensembles depends on the assumed s…

Ensemble learning is a powerful paradigm that has been usedin the top state-of-the-art machine learning methods like Random Forestsand XGBoost. Inspired by the success of such methods, we have devel-oped a new Genetic Programming method called Ensemble GP. The evo-lutionary cycle of Ensemble GP follows the same steps a…

This work investigates power laws in deep neural network ensembles and predicts their performance.

Residual networks' depth is mathematically equivalent to expanding an implicit ensemble size.

Ensembles of neural networks learn better by sharing information.

An ensemble of neural networks is known to be more robust and accurate than an individual network, however usually with linearly-increased cost in both training and testing. In this work, we propose a two-stage method to learn Sparse Structured Ensembles (SSEs) for neural networks. In the first stage, we run SG-MCMC wi…

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 proposes a new method for probabilistic electricity price forecasting.

Study on ensemble, distillation, and self-distillation in deep learning models.

Deep ensembles outperform deep ensembles of Bayesian neural networks on in-distribution data.

TREX explains tree ensembles by identifying key training examples.

ROAD-EnKFs use learned low-dimensional models to improve state reconstruction and forecasting.

Develops an empirical likelihood framework for random forests and ensembles.

Deep learning ensemble improves Alzheimer vs. Mild Cognitive Impairment diagnosis.

An ensemble technique is characterized by the mechanism that generates the components and by the mechanism that combines them. A common way to achieve the consensus is to enable each component to equally participate in the aggregation process. A problem with this approach is that poor components are likely to negativel…

Ensemble learning is a mainstay in modern data science practice. Conventional ensemble algorithms assigns to base models a set of deterministic, constant model weights that (1) do not fully account for variations in base model accuracy across subgroups, nor (2) provide uncertainty estimates for the ensemble prediction,…

The success of deep learning in medical imaging is mostly achieved at the cost of a large labeled data set. Semi-supervised learning (SSL) provides a promising solution by leveraging the structure of unlabeled data to improve learning from a small set of labeled data. Self-ensembling is a simple approach used in SSL to…

This paper tackles robustness of ensemble stumps and trees under general ℓ_p norm perturbations.

Spectral analysis detects structural changes in financial networks.

Proposes a deep tree-ensemble model for multi-output prediction.

A new method improves efficiency of conformal prediction for ensemble models.

A graph neural network improves multivariate post-processing of ensemble forecasts.

EntroPath learns manifold geometry from diffusion paths.