arXiv research

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…

New nonlinear smoothers improve state estimation in chaotic systems.

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

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…

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.

dSMC improves parallel processing of state-space models.

Combining deep learning and ensemble smoothers for better history matching.

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…

EnKBS smoothes complex systems with future observations for causal inference.

Improved Thompson Sampling for smoother functions with noise.

We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and …

Convolutional Neural Networks (CNN) and the locally connected layer are limited in capturing the importance and relations of different local receptive fields, which are often crucial for tasks such as face verification, visual question answering, and word sequence prediction. To tackle the issue, we propose a novel loc…

Develops a novel ML smoothing method for incomplete data in state-space models.

ResNets promote smoother interpolations than MLPs, enhancing generalization.

Bayesian convolutional deep sets improve ambiguity in stationary process modeling.

Auto-regressive models improve smoothing efficiency with exponentially tapered windows.

Paper proposes a new Taylor moment expansion for non-linear Gaussian filtering and smoothing.

We introduce a new algorithm, called adaptive sparse backfitting algorithm, for solving high dimensional Sparse Additive Model (SpAM) utilizing symmetric, non-negative definite smoothers. Unlike the previous sparse backfitting algorithm, our method is essentially a block coordinate descent algorithm that guarantees to …

New method for robust fixed-point smoothing without state augmentation.

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…

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

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

Paper uses SLT to improve model selection for SHM.

Unified framework for efficient Gaussian process inference.

Paper uses UKS to improve BLE RSSI for proximity inference in mobile phone apps.

Optimal tuning of Tikhonov regularizers achieves best performance without additional cost.

Generative models learn smoother densities to sample from unknown distributions.

WGAN uses a smoother metric to train GANs better.

Bayesian approach improves neural network recurrence.

Unified approach to multiclass classification using Gabriel graphs.

We describe notions of tautness that arise in the study of $C^0$ foliations, $C^{1,0}$ or smoother foliations, and in geometry. We give examples to show that these notions are different, and discuss how these differences impact some classical foliation results. We construct examples of smoothly taut $C^{\infty,0}$ foli…

New method calibrates LV surfaces for exotic derivatives with smoother, more stable Greeks.

Study RKHS on manifolds, linking Sobolev and diffusion spaces.

The ropelength of a knot is the quotient of its length and its thickness, the radius of the largest embedded normal tube around the knot. We prove existence and regularity for ropelength minimizers in any knot or link type; these are $C^{1,1}$ curves, but need not be smoother. We improve the lower bound for the ropelen…

Study applies HRP to Latin American markets, showing smoother risk-return profile.

Deep model predicts shapes of curves with multiple covariates.

GDM models time series with smoother transitions and interpretable states.

Develops state-space deep Gaussian processes for irregular signals.

We introduce a family of adaptive estimators on graphs, based on penalizing the $\ell_1$ norm of discrete graph differences. This generalizes the idea of trend filtering [Kim et al. (2009), Tibshirani (2014)], used for univariate nonparametric regression, to graphs. Analogous to the univariate case, graph trend filteri…

Introduces Gaussian Processes and Relevance Vector Machines, connecting them to Kalman filtering.

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

Can the Minkowski sum of two compact convex bodies be made smoother by rotating one of them? We construct two infinitely differentiable strictly convex plane bodies such that after any generic rotation (in the Baire category sense) of one of the summands the Minkowski sum is not five times differentiable. On the other …

Method approximates Lipschitz domains with smoother shapes.

Policy-gradient method controls multiple non-cohesive targets.

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…

We present a novel synthesis of Fisher information and asset pricing theory that yields a practical method for reconstructing the probability density implicit in security prices. The Fisher information approach to these inverse problems transforms the search for a probability density into the solution of a differential…

This paper presents a tensor multiplication based smoothing algorithm that follows a two step denoising method. Unlike other traditional averaging approaches, our approach uses an element based normal voting tensor to compute smooth surfaces. By introducing a binary optimization on the proposed tensor together with a l…

We investigate the learning rate of multiple kernel leaning (MKL) with elastic-net regularization, which consists of an $\ell_1$-regularizer for inducing the sparsity and an $\ell_2$-regularizer for controlling the smoothness. We focus on a sparse setting where the total number of kernels is large but the number of non…