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.
dSMC improves parallel processing of state-space models.
Develops a novel ML smoothing method for incomplete data in state-space models.
We consider the approximation of expectations with respect to the distribution of a latent Markov process given noisy measurements. This is known as the smoothing problem and is often approached with particle and Markov chain Monte Carlo (MCMC) methods. These methods provide consistent but biased estimators when run fo…
Particle Metropolis-Hastings (PMH) allows for Bayesian parameter inference in nonlinear state space models by combining Markov chain Monte Carlo (MCMC) and particle filtering. The latter is used to estimate the intractable likelihood. In its original formulation, PMH makes use of a marginal MCMC proposal for the parame…
We prove that for any convex globally hyperbolic maximal (GHM) anti-de Sitter (AdS) 3-dimensional space-time with particles (cone singularities of angles less than along time-like curves), the complement of the convex core in admits a unique foliation by constant Gauss curvature surfaces. This extends, and …
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.
A new algorithm reduces bias in estimating model parameters.
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 Boosting algorithm and provides a new statistical interpretation for 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.
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.
The paper is concerned with non-linear Gaussian filtering and smoothing in continuous-discrete state-space models, where the dynamic model is formulated as an Itô stochastic differential equation (SDE), and the measurements are obtained at discrete time instants. We propose novel Taylor moment expansion (TME) Gaussian …
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…
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.
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.
A computer vision approach improves neutral particle detection in particle flow algorithms.
Jointly estimates flow fields and particle properties from Lagrangian data.
Estimates log-likelihood of interacting particle systems using virtual particles.
This paper optimizes functions of probability measures using particle gradient descent for displacement convex functions.
The excluded area between a pair of two-dimensional hard particles with given relative orientation is the region in which one particle cannot be located due to the presence of the other particle. The magnitude of the excluded area as a function of the relative particle orientation plays a major role in the determinatio…
Differentiable resampling improves particle filter performance.
Paper uses SLT to improve model selection for SHM.
A method for optimal Bayesian filtering using progressive particle flow and optimal transport maps.
Optimal weights improve particle-based approximations of discrete distributions.
Particle MCMC involves using a particle filter within an MCMC algorithm. For inference of a model which involves an unobserved stochastic process, the standard implementation uses the particle filter to propose new values for the stochastic process, and MCMC moves to propose new values for the parameters. We show how p…
Unified framework for efficient Gaussian process inference.
Improved particle approximation for mean-field neural networks.
A new particle filter avoids resampling to improve state estimation in high dimensions.
We introduce a new sequential Monte Carlo algorithm we call the particle cascade. The particle cascade is an asynchronous, anytime alternative to traditional particle filtering algorithms. It uses no barrier synchronizations which leads to improved particle throughput and memory efficiency. It is an anytime algorithm i…
Paper uses UKS to improve BLE RSSI for proximity inference in mobile phone apps.
Paper uses averaging from many particle filters to approximate posterior predictive distributions.
Cryo-electron microscopy (cryoEM) is an increasingly popular method for protein structure determination. However, identifying a sufficient number of particles for analysis (often >100,000) can take months of manual effort. Current computational approaches are limited by high false positive rates and require significant…
This paper studies when particle filtering is efficient for planning in partially observed systems.
Interacting particle methods are increasingly used to sample from complex and high-dimensional distributions. These stochastic particle integration techniques can be interpreted as an universal acceptance-rejection sequential particle sampler equipped with adaptive and interacting recycling mechanisms. Practically, the…
Cryo-electron microscopy (cryo-EM) is an emerging experimental method to characterize the structure of large biomolecular assemblies. Single particle cryo-EM records 2D images (so-called micrographs) of projections of the three-dimensional particle, which need to be processed to obtain the three-dimensional reconstruct…
New algorithm trains latent diffusion models using interacting particles.
Particle-optimization-based sampling (POS) is a recently developed effective sampling technique that interactively updates a set of particles. A representative algorithm is the Stein variational gradient descent (SVGD). We prove, under certain conditions, SVGD experiences a theoretical pitfall, {\it i.e.}, particles te…
Framework expands particle filtering to estimate states beyond prior boundaries.