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.
A framework preserves uncertainty in ensemble distillation.
New framework uses tree ensembles for contextual bandits.
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…
Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space. Accuracy and diversity serve as two crucial factors while they usually conflict with each other. To balance both of them, we formalize the ensemble pruni…
Flexible tree ensemble learning framework supports arbitrary loss functions and multi-task learning.
Unified framework for cost-sensitive ensemble learning.
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…
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 …
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 …
Simulator imperfection, often known as model error, is ubiquitous in practical data assimilation problems. Despite the enormous efforts dedicated to addressing this problem, properly handling simulator imperfection in data assimilation remains to be a challenging task. In this work, we propose an approach to dealing wi…
Unified framework for ensemble transport-based smoothing of non-Gaussian time series.
Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based le…
This research tackles uncertainty in gradient boosting models using ensemble methods.
Improved regret bound for linear ensemble sampling.
Recognizing apparel attributes has recently drawn great interest in the computer vision community. Methods based on various deep neural networks have been proposed for image classification, which could be applied to apparel attributes recognition. An interesting problem raised is how to ensemble these methods to furthe…
Ensemble of GANs improves performance on disconnected data.
New framework learns interpretable rule ensembles without sacrificing accuracy.
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.…
Paper proposes GP-NAS-ensemble for fast neural architecture performance prediction.
New framework detects model weaknesses in decision tree ensembles.
VGE provides a practical approach to uncertainty estimation in ensemble models.
A new framework combines multiple loss reserving models for better predictive performance.
Paper proposes ensemble distillation for well-calibrated structured prediction.
In unsupervised outlier ensembles, the absence of ground truth makes the combination of base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles lack a reliable way of selecting competent base detectors, affecting accuracy and stability, during model combination. In this paper, we pr…
Develops RKHS framework for analyzing tree ensembles.
Enhances fairness in predictions without sacrificing accuracy.
TREX explains tree ensembles by identifying key training examples.
Existing information-theoretic frameworks based on maximum entropy network ensembles are not able to explain the emergence of heterogeneity in complex networks. Here, we fill this gap of knowledge by developing a classical framework for networks based on finding an optimal trade-off between the information content of a…
Develops an empirical likelihood framework for random forests and ensembles.
Ensemble learning use multiple algorithms to obtain better predictive performance than any single one of its constituent algorithms could. With growing popularity of deep learning, researchers have started to ensemble them for various purposes. Few if any, however, has used the deep learning approach as a means to ense…
AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework for automatically learning high-quality ensembles with minimal expert intervention. Our framework is inspired by the AdaNet algorithm (Cortes et al., 2017) which learns the structure of a neural network as an ensemble of subnetworks. We designed it…
Proposes a three-stage debiasing framework to improve out-of-distribution accuracy.
Study develops ensemble machine learning framework for predicting groundwater heavy metal pollution.
Gradient-free ensemble learns sector forecasts from diverse models.
Proposes an ensemble framework combining VAE, Transformer, and LSTM for stock price prediction.
Paper presents a Bayesian-decision-theory framework for long-tailed classification.
Paper analyzes ensemble Kalman updates for effective dimension and localization.
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 (…
We propose an algorithm to enhance certified robustness of a deep model ensemble by optimally weighting each base model. Unlike previous works on using ensembles to empirically improve robustness, our algorithm is based on optimizing a guaranteed robustness certificate of neural networks. Our proposed ensemble framewor…
ForestPrune optimizes tree ensemble pruning for compactness and speed.
A new method improves efficiency of conformal prediction for ensemble models.
A new machine learning framework called machine collaboration improves prediction accuracy.
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…
Unified framework for portfolio optimization using multiple hypotheses.
Ensemble++ uses shared-factor ensembles to scale Thompson Sampling for linear and nonlinear bandits.
Paper proposes ECOC for deep neural network ensembles to improve performance.
Temporal networks are ubiquitous and evolve over time by the addition, deletion, and changing of links, nodes, and attributes. Although many relational datasets contain temporal information, the majority of existing techniques in relational learning focus on static snapshots and ignore the temporal dynamics. We propose…
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…