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…
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48 results for “Ensemble learning”
Self-paced ensemble learning improves audio classification models.
problem Improving performance of individual models in speech and audio classification.
method A self-paced ensemble learning scheme where models learn from each other over several iterations.
result SPEL significantly outperforms baseline ensemble models.
Auto-Ensemble automates deep learning model ensembling with adaptive learning rate scheduling.
problem Difficulty in collecting diverse and accurate deep learning models through single training.
method Auto-Ensemble collects model checkpoints and uses adaptive learning rate scheduling to ensemble them.
result Ensembled models converge to various local optima, improving performance on few-shot learning.
Paper analyzes uncertainty metrics in ensemble learning for healthcare AI.
problem Selecting appropriate uncertainty metrics for ensemble learners in healthcare AI.
method Rigorous analysis of two uncertainty metrics: ensemble mean and variance.
result Ensemble mean is preferable to ensemble variance for decision making in healthcare AI.
New method improves ensemble quality by exploring the pre-train basin more effectively.
problem Limited diversity in ensembles trained from a single pre-trained checkpoint.
method Proposed StarSSE modification of Snapshot Ensembles for transfer learning.
result Stronger ensembles and uniform model soups achieved.
SPQR improves Q-ensemble diversity in reinforcement learning.
problem Overestimation bias in Q-learning for complex tasks.
method Introduces SPQR for Q-ensemble independence regularization.
result SPQR outperforms baseline algorithms in online and offline RL benchmarks.
In this paper, we propose to provide a general ensemble learning framework based on deep learning models. Given a group of unit models, the proposed deep ensemble learning framework will effectively combine their learning results via a multilayered ensemble model. In the case when the unit model mathematical mappings a…
Preventing Value Function Collapse in Ensemble {Q}-Learning by Maximizing Representation Diversitycs.LG
New technique prevents Q-learning collapse by maximizing diversity among ensembles.
problem Value function collapse in ensemble Q-learning.
method Maximizing representation diversity through regularization.
result Regularized approach significantly outperforms existing methods.
Enhanced reinforcement learning using ensemble methods.
problem Improving reinforcement learning performance.
method Distributional reinforcement learning with ensemble group-aided training.
result Ensemble methods lead to more robust and efficient learning.
New framework learns interpretable rule ensembles without sacrificing accuracy.
problem Trade-off between accuracy and interpretability in rule ensembles.
method Introduces local interpretability and a regularizer to promote it, using coordinate descent with local search.
result Learns rule ensembles with fewer rules to explain individual predictions, maintaining comparable accuracy.
Fibonacci Ensembles use Fibonacci weights to improve ensemble learning, inspired by natural growth patterns.
problem Improving ensemble learning methods to enhance model performance and interpretability.
method Introduces Fibonacci weights and a recursive ensemble dynamic to reduce variance and enrich representational depth.
result Fibonacci weighting can match or improve upon uniform averaging in ensemble learning experiments.
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learningstat.ML
Efficiently builds diverse sub-model ensembles for robust self-supervised learning.
problem Challenges in diversity and efficiency of deep ensembles for self-supervised representation learning.
method Ensemble of independent sub-networks with a new loss function for diversity.
result Significantly improves prediction reliability and model calibration.
Flexible tree ensemble learning framework supports arbitrary loss functions and multi-task learning.
problem Limited modeling capabilities of existing tree ensemble learning toolkits.
method Differentiable tree ensembles with tensor-based formulation for efficient training.
result Our framework leads to 100x more compact and 23% more expressive tree ensembles.
Ensembles of neural networks learn better by sharing information.
problem Improving performance of neural networks through collective learning.
method Modeling neural networks as socially interacting agents aiming to maximize their own performance and functional relations to others.
result Optimal collective performance emerges from local interactions between networks, leading to specialization and higher confidence.
Study on ensemble, distillation, and self-distillation in deep learning models.
problem Improving test accuracy in deep learning models using ensemble and distillation methods.
method Formal study of ensemble and distillation, considering multi-view data structure.
result Proven that ensemble and distillation can improve test accuracy in deep learning models, and the superior performance can be distilled into a single model.
Theory and method for reducing prediction variance in noisy feature-subsampled ridge ensembles.
problem Reduction of prediction variance in noisy data with feature bagging.
method Developed analytical learning curves for noisy ridge ensembles, introduced heterogeneous feature ensembling.
result Subsampling shifts the double-descent peak, leading to improved performance over a single linear predictor.
Deep interpretable ensemblesstat.ML
Transforms ensemble predictions to maintain interpretability.
problem Loss of interpretability in deep ensembles.
method Proposes transformation ensembles that aggregate predictions while preserving interpretability.
result Transformation ensembles yield better predictions than individual models and maintain interpretability.
Quantum machine learning uses superposition to create a large ensemble of classifiers.
problem Improving machine learning efficiency on quantum computers.
method Using superposition to create an exponentially large ensemble of classifiers, trained with an optimization-free learning algorithm.
result Adding an optimization step improves the performance of quantum ensembles of classifiers.
Proposes a new method to enhance neural learning by maximizing information gain.
problem Improving neural learning by selecting key variables to maximize information gain.
method Adaptive Ensemble Kalman Filter to quantify uncertainty and maximize information gain.
result The proposed method enables the neural network to learn more effectively from stochastic systems.
In this article supervised learning problems are solved using soft rule ensembles. We first review the importance sampling learning ensembles (ISLE) approach that is useful for generating hard rules. The soft rules are then obtained with logistic regression from the corresponding hard rules. In order to deal with the p…
Proposes a new method for deep ensembles that improves accuracy and calibration.
problem Improving accuracy and calibration of deep ensembles.
method Estimates confusion matrices of ensemble members and weighs them according to their inferred performance.
result Empirically shows superiority of soft Dawid Skene over ensemble averaging.
GNCL algorithm controls diversity in deep ensembles.
problem Managing bias and variance in deep ensembles.
method Generalized bias-variance decomposition for arbitrary loss functions, leading to GNCL algorithm.
result Explicit control over ensemble diversity and smooth interpolation between independent and joint training.
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…
DASH improves ensemble generalizability by encouraging diverse, flat loss landscapes.
problem Improving generalization and robustness of deep ensembles.
method DASH promotes diversity and flatness in deep ensembles by encouraging base learners to move towards low-loss regions of minimal sharpness.
result DASH improves ensemble generalizability, as demonstrated by extensive empirical evidence.
This paper studies recursive ensembles driven by Fibonacci updates, improving learning dynamics.
problem Improving learning dynamics in recursive ensemble learning.
method Develops second-order recursive architectures with Fibonacci-type update flows.
result Establishes global convergence conditions and generalization bounds for recursive ensembles.
When constructing a classifier ensemble, diversity among the base classifiers is one of the important characteristics. Several studies have been made in the context of standard static data, in particular, when analyzing the relationship between a high ensemble predictive performance and the diversity of its components.…
Ensemble learning is a very prevalent method employed in machine learning. The relative success of ensemble methods is attributed to their ability to tackle a wide range of instances and complex problems that require different low-level approaches. However, ensemble methods are relatively less popular in reinforcement …
This study extends verifiable learning to boosted tree ensembles, enabling efficient security verification.
problem Efficiently verifying the robustness of boosted tree ensembles against norm-based attackers.
method Formal verification of robustness for large-spread boosted tree ensembles, considering -norm and pseudo-polynomial time for -norm verification.
result Polynomial time verification for -norm attackers, NP-hard for other norms, and pseudo-polynomial time for -norm verification.
This paper develops a new theory for ensemble learning beyond variance reduction.
problem Ensemble learning's effectiveness for stable estimators is not fully explained by variance reduction.
method Develops a general weighting theory for ensemble learning, formalizing ensembles as linear operators and introducing geometric and spectral constraints.
result Structured weights can outperform uniform averaging by reshaping approximation geometry and redistributing spectral complexity.
Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systemsmath.DS
This paper proves long-time accuracy of ensemble Kalman filters for chaotic and machine-learned systems.
problem Ensuring long-term accuracy of ensemble Kalman filters for complex dynamical systems.
method Established conditions for long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems.
result Ensemble Kalman filters maintain small estimation error over long time horizons for chaotic and machine-learned systems.
Single model estimates ensemble uncertainty efficiently.
problem Efficient uncertainty quantification in deep learning models.
method Contextual similarity distillation, approximating ensemble variance with a single model.
result Single model estimates predictive variance with a single forward pass.
New approach verifies tree ensembles efficiently, making them more robust.
problem Verifying robustness of decision tree ensembles against evasion attacks is NP-hard.
method Identified large-spread ensembles, proposed verifiable learning, designed training algorithm.
result Large-spread ensembles can be verified in polynomial time and are more robust.
Developing parsimonious ensembles using ensemble diversity within a reinforcement learning frameworkcs.LG
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 …
This paper improves SSL methods using ensemble techniques with data-dependent weighted losses.
problem Improving SSL performance and robustness with large unlabeled data.
method Developed a framework for weighted cross-entropy losses in ensembling SSL methods without altering the backbone.
result Our method outperforms state-of-the-art SSL methods on ImageNet-1K, especially in few-shot learning.
Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta-learning framework, ensemble techniques can easily be applied to many machine learning methods. Inspired by ensemble techniques, in this paper we propose an ensemble loss functions applied to a simple regre…
Ensemble learning improves anomaly detection for milder symptoms.
problem Difficulty in detecting incipient anomalies due to similarity to normal conditions.
method Utilize uncertainty information from ensemble learning to identify misclassified incipient anomalies.
result Ensemble learning methods show improved performance on incipient anomaly detection.
Jointly tuning ensemble models improves performance and uncertainty calibration.
problem Improving both predictive performance and uncertainty calibration in deep ensembles.
method Investigated the impact of jointly tuning weight decay, temperature scaling, and early stopping.
result Jointly tuning ensemble models generally matches or improves performance, with significant variation across tasks.
AEA dynamically aggregates ensemble targets for actor-critic learning.
problem Static ensemble aggregation methods struggle with overestimation bias and variance.
method Adaptive Ensemble Aggregation (AEA) dynamically constructs ensemble-based targets.
result AEA converges to optimal variance reduction and maximal Fisher information.
Fed-ensemble improves FL by averaging predictions from multiple models.
problem Improving generalization in federated learning.
method Random permutations to update K models, averaging predictions.
result Predictions from all K models have the same predictive posterior distribution.
Gradient-free ensemble learns sector forecasts from diverse models.
problem Predicting sector returns in a volatile market.
method Dynamic model combination using out-of-sample R-squared.
result Ensemble outperforms individual models in sector rotation.
Logifold improves ensemble machine learning by identifying fuzzy domains.
problem Improving ensemble machine learning accuracy.
method Formulating logifold structure and interpreting local charts of datasets.
result Logifold improves accuracy compared to averaging model outputs.
Neural networks learn from ensemble forecasts without considering their order.
problem Improving reliability of probabilistic weather forecasts.
method Permutation-invariant neural networks for postprocessing ensemble forecasts.
result Models achieve state-of-the-art prediction quality in surface temperature and wind gust forecasts.
The use of ensembles of neural networks (NNs) for the quantification of predictive uncertainty is widespread. However, the current justification is intuitive rather than analytical. This work proposes one minor modification to the normal ensembling methodology, which we prove allows the ensemble to perform Bayesian inf…
An ensemble method enhances cryptocurrency trading strategies using deep reinforcement learning.
problem Improving generalization performance in stochastic cryptocurrency trading environments.
method Model selection and mixture distribution policy to ensemble deep reinforcement learning models.
result Improved out-of-sample performance compared to benchmarks.
Gestalt combines two models to improve SQuAD2.0 performance.
problem Improving the accuracy of answering questions in context paragraphs.
method A stacking ensemble of ALBERT and RoBERTa models, combined with a CNN-based meta-model.
result Best ensemble achieved 87.117 EM and 90.306 F1 scores, improving baseline by 0.55% and 0.61% respectively.
Deep RL ensemble strategy outperforms individual algorithms in stock trading.
problem Designing profitable stock trading strategies in a complex market.
method Ensemble of three deep reinforcement learning algorithms (PPO, A2C, DDPG) for stock trading.
result Deep ensemble strategy outperforms individual algorithms and traditional min-variance portfolio.
Ensembles, where multiple neural networks are trained individually and their predictions are averaged, have been shown to be widely successful for improving both the accuracy and predictive uncertainty of single neural networks. However, an ensemble's cost for both training and testing increases linearly with the numbe…
A Framework for an Assessment of the Kernel-target Alignment in Tree Ensemble Kernel Learningstat.ML
The study investigates kernel-target alignment in tree ensemble kernels.
problem The degree of kernel-target alignment affects the performance of tree ensemble kernels in kernel learning.
method Eigenanalysis of the kernel matrix and sensitivity analysis via landmark learning.
result Good performance of tree ensemble kernels is associated with strong kernel-target alignment.