arXiv research

New nonlinear smoothers improve state estimation in chaotic systems.

Combining deep learning and ensemble smoothers for better history matching.

EnKBS smoothes complex systems with future observations for causal inference.

Unified framework for ensemble transport-based smoothing of non-Gaussian time series.

Paper explains how tree ensembles improve predictions by smoothing and regulating smoothness.

Workflow uses deep learning to improve geosteering accuracy in Goliat Field.

Forest-guided smoothing uses random forest outputs for interpretable local smoothers.

Study uses DNNs for real-time EM inversion, highlighting model errors and proposing solutions.

We propose a novel algorithm for large-scale regression problems named histogram transform ensembles (HTE), composed of random rotations, stretchings, and translations. First of all, we investigate the theoretical properties of HTE when the regression function lies in the Hölder space $C^{k,α}$, $k \in \mathbb{N}_0$, $…

Data assimilation for subsurface flow using latent diffusion models shows that ensemble Kalman methods may overestimate posterior uncertainty, while Monte Carlo sampling is more reliable.

The literature about history matching is vast and despite the impressive number of methods proposed and the significant progresses reported in the last decade, conditioning reservoir models to dynamic data is still a challenging task. Ensemble-based methods are among the most successful and efficient techniques current…

Flexible tree ensemble learning framework supports arbitrary loss functions and multi-task learning.

Estimating the state of a dynamical system from a series of noise-corrupted observations is fundamental in many areas of science and engineering. The most well-known method, the Kalman smoother (and the related Kalman filter), relies on assumptions of linearity and Gaussianity that are rarely met in practice. In this p…

New methods improve tree ensemble models by compressing them while maintaining accuracy.

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

We present a Kalman smoothing framework based on modeling errors using the heavy tailed Student's t distribution, along with algorithms, convergence theory, open-source general implementation, and several important applications. The computational effort per iteration grows linearly with the length of the time series, a…

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 …

We investigate an algorithm named histogram transform ensembles (HTE) density estimator whose effectiveness is supported by both solid theoretical analysis and significant experimental performance. On the theoretical side, by decomposing the error term into approximation error and estimation error, we are able to condu…

Spatial smoothing improves BNNs' accuracy, uncertainty, and robustness without increasing computational cost.

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.…

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…

BI-EqNO improves Bayesian inference with flexible neural operators.

Combines deep generative models with ensemble methods for subsurface property estimation.

This paper presents a general iterative bias correction procedure for regression smoothers. This bias reduction schema is shown to correspond operationally to the $L_2$ Boosting algorithm and provides a new statistical interpretation for $L_2$ Boosting. We analyze the behavior of the Boosting algorithm applied to commo…

Method approximates Lipschitz domains with smoother shapes.

Exponential smoothers are a simple and memory efficient way to compute running averages of time series. Here we define and describe practical properties of exponential smoothers for signals observed at constant and variable intervals.

ControlBurn selects few features from tree ensembles for better model interpretability.

CDST improves ensemble prediction by adjusting model weights based on covariates.

dSMC improves parallel processing of state-space models.

Generative adversarial network improves geosteering in fluvial reservoirs.

Paper improves training physics-informed neural networks with model ensembles.

The recent success of Deep Neural Networks (DNNs) has drastically improved the state of the art for many application domains. While achieving high accuracy performance, deploying state-of-the-art DNNs is a challenge since they typically require billions of expensive arithmetic computations. In addition, DNNs are typica…

Study models weather index insurance pricing by insurers and farmers, finding flexible pricing kernels boost profits.

MAC combines models without locking them, improving ensemble performance.

New ensemble models classify mouse movement trajectories to assess survey question difficulty.

We present power low rank ensembles (PLRE), a flexible framework for n-gram language modeling where ensembles of low rank matrices and tensors are used to obtain smoothed probability estimates of words in context. Our method can be understood as a generalization of n-gram modeling to non-integer n, and includes standar…

Both the median-based classifier and the quantile-based classifier are useful for discriminating high-dimensional data with heavy-tailed or skewed inputs. But these methods are restricted as they assign equal weight to each variable in an unregularized way. The ensemble quantile classifier is a more flexible regularize…

Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships between features and predictions, we propose an alternative approach to interpret tree ensemble classifiers by surfacing representative poin…

Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design. To achieve better performance, however, the searching process usually costs massive computations that might not be affordable for researcher…

When randomized ensemble methods such as bagging and random forests are implemented, a basic question arises: Is the ensemble large enough? In particular, the practitioner desires a rigorous guarantee that a given ensemble will perform nearly as well as an ideal infinite ensemble (trained on the same data). The purpose…

Parallel-in-time solver reduces ODE simulation time from linear to logarithmic.

Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that i…

Decentralized Gaussian processes for multi-agent systems.

New algorithm reduces overfitting in neural networks.

Proposes a new method for ensembling neural subnetworks.

Ensembling smaller models can outperform larger models in terms of accuracy and efficiency.

We consider a self-exciting counting process, the parameters of which depend on a hidden finite-state Markov chain. We derive the optimal filter and smoother for the hidden chain based on observation of the jump process. This filter is in closed form and is finite dimensional. We demonstrate the performance of this fil…

Improved wind speed forecasts for power generation using machine learning.