A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Researchers improve deep ensemble forecast aggregation methods.
AEA dynamically aggregates ensemble targets for actor-critic learning.
Proposes a new method for deep ensembles that improves accuracy and calibration.
This work studies a unified approach to ensemble aggregation using likelihood perspective.
This paper explores methods for combining predictions in multilabel classification.
Partial fusion combines neural networks to balance accuracy and efficiency.
Transforms ensemble predictions to maintain interpretability.
Paper introduces WWAggr for ensemble CPD, improving accuracy and decision threshold selection.
A new method improves efficiency of conformal prediction for ensemble models.
Unified approach to aggregating models and preferences.
Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods. Many of these strategies have been developed in a supervised setting, where the accuracy of each base classifier can be empirically measured and this information is incorporated in the…
We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data. We find the minimax optimal predictions for a very general class of loss functions including all convex and many non-conv…
A new stochastic method handles ensemble creation with cost constraints.
FedBE aggregates local models into a robust global model via Bayesian inference.
The Wisdom of Crowds is a phenomenon described in social science that suggests four criteria applicable to groups of people. It is claimed that, if these criteria are satisfied, then the aggregate decisions made by a group will often be better than those of its individual members. Inspired by this concept, we present a…
We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with CO…
The paper presents a method for generating well-calibrated prediction intervals using quality-driven deep ensembles.
MNIST-NET10 fusion improves MNIST classification to 0.1% error rate.
A new method aggregates generative classifiers to resist adversarial attacks.
This study examines ensembling of diffusion models for improved generative quality.
Ensemble models provide more accurate feature importance estimates than single models.
The number of component classifiers chosen for an ensemble greatly impacts the prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble size, which is applicable to most of existing batch and online ensemble classifiers. There are only a limited number of studies on the ense…
Probabilistic graphical models are graphical representations of probability distributions. Graphical models have applications in many fields including biology, social sciences, linguistic, neuroscience. In this paper, we propose directed acyclic graphs (DAGs) learning via bootstrap aggregating. The proposed procedure i…
The Wisdom of Crowds (WOC), as a theory in the social science, gets a new paradigm in computer science. The WOC theory explains that the aggregate decision made by a group is often better than those of its individual members if specific conditions are satisfied. This paper presents a novel framework for unsupervised an…
Randomized gradient-based ensemble improves prediction accuracy.
WildWood improves Random Forest predictions using bootstrap out-of-bag samples.
New method improves CATE model selection with optimal regret rates.
Ensemble methods for classification and clustering have been effectively used for decades, while ensemble learning for outlier detection has only been studied recently. In this work, we design a new ensemble approach for outlier detection in multi-dimensional point data, which provides improved accuracy by reducing err…
New method for disaggregate electricity demand forecasting at household level.
E-CIT framework reduces CITs' computational burden and improves causal discovery performance.
Improved sales forecasting at various levels using ensemble methods.
This paper presents first steps toward robust models for crisis prediction. We conduct a horse race of conventional statistical methods and more recent machine learning methods as early-warning models. As individual models are in the literature most often built in isolation of other methods, the exercise is of high rel…
Ensemble learning is a powerful approach to construct a strong learner from multiple base learners. The most popular way to aggregate an ensemble of classifiers is majority voting, which assigns a sample to the class that most base classifiers vote for. However, improved performance can be obtained by assigning weights…
New ensemble method improves model stability exponentially.
Fed-ensemble improves FL by averaging predictions from multiple models.
The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics, namely the prediction of genetic interactions and protein functi…
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…
The rapid adoption of machine learning has increased concerns about the privacy implications of machine learning models trained on sensitive data, such as medical records or other personal information. To address those concerns, one promising approach is Private Aggregation of Teacher Ensembles, or PATE, which transfer…
Quality assessments of models in unsupervised learning and clustering verification in particular have been a long-standing problem in the machine learning research. The lack of robust and universally applicable cluster validity scores often makes the algorithm selection and hyperparameter evaluation a tough guess. In t…
This paper adapts PATE for semantic segmentation while maintaining privacy.
We propose a novel "tree-averaging" model that utilizes the ensemble of classification and regression trees (CART). Each constituent tree is estimated with a subset of similar data. We treat this grouping of subsets as Bayesian ensemble trees (BET) and model them as an infinite mixture Dirichlet process. We show that B…
Aggregating multiple learners through an ensemble of models aim to make better predictions by capturing the underlying distribution of the data more accurately. Different ensembling methods, such as bagging, boosting, and stacking/blending, have been studied and adopted extensively in research and practice. While baggi…
Ensemble model predicts AD progression from CN status with high accuracy.
Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training data. This paper presents a unifying framework for supervised classification with general ensembles of training dat…
Recent studies have shown that the aggregated dynamic flexibility of an ensemble of thermostatic loads can be modeled in the form of a virtual battery. The existing methods for computing the virtual battery parameters require the knowledge of the first-principle models and parameter values of the loads in the ensemble.…
The paper analyzes bootstrap ensemble classifiers in high-dimensional settings.
Random Forests automatically prune a latent 'true' tree, explaining their overfitting without tuning.
New research shows deep models learn sparse features, limiting transfer learning; ensembling improves performance.