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.
Paper analyzes ensemble Kalman updates for effective dimension and localization.
EnKF's update is shown to be similar to Matheron's method in Gaussian process regression.
Proposes a new method for nonlinear Bayesian updates using ensemble kernel regression.
Many nonlinear extensions of the Kalman filter, e.g., the extended and the unscented Kalman filter, reduce the state densities to Gaussian densities. This approximation gives sufficient results in many cases. However, this filters only estimate states that are correlated with the observation. Therefore, sequential esti…
Enhances linear regression with Kalman filter for loss minimization.
Graph Kalman filters adapt classical filters to graph data.
A new filter reduces density fitting to a linear solve, improving performance on nonlinear systems.
In this paper, we propose a probabilistic optimization method, named probabilistic incremental proximal gradient (PIPG) method, by developing a probabilistic interpretation of the incremental proximal gradient algorithm. We explicitly model the update rules of the incremental proximal gradient method and develop a syst…
Robust Kalman filtering method for outlier detection.
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.
In this work, we highlight a connection between the incremental proximal method and stochastic filters. We begin by showing that the proximal operators coincide, and hence can be realized with, Bayes updates. We give the explicit form of the updates for the linear regression problem and show that there is a one-to-one …
Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate serious…
In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) (Kalman et al., 1960) have been integrated with deep learning models, however, such approaches typically rely on approximate inference techniques such as variational inference which makes learning more complex and often le…
A new method reduces high-dimensional filtering to quadratic complexity.
FAKI improves gradient-free inference for inverse problems.
Proposes variational Gaussian approximations for solving the Kushner equation.
A new ML-based filter improves data assimilation for nonlinear systems.
Proposes LAE-EnKF for improved nonlinear data assimilation.
Unified framework for efficient Gaussian process inference.
This paper explores estimating chaotic dynamics and parameters using local ensemble Kalman filters.
A new filter adapts to heavy-tailed data without tuning, improving performance in challenging conditions.
Improved robustness for high-dimensional Kalman filtering.
A new model detects anomalies in time series data efficiently.
This work preserves linear invariants in ensemble filters for non-Gaussian data assimilation.
Unified framework for ensemble transport-based smoothing of non-Gaussian time series.
RCUKF combines data-driven modeling and Bayesian estimation for accurate system state estimation.
CG-EnKF and NS-EnKF outperform deep learning-based SF in data assimilation.
This paper explores online learning of dynamics and state using ensemble Kalman filters.
High fidelity behavior prediction of intelligent agents is critical in many applications. However, the prediction model trained on the training set may not generalize to the testing set due to domain shift and time variance. The challenge motivates the adoption of online adaptation algorithms to update prediction model…
Sparse Markovian Gaussian processes improve probabilistic model inference for large datasets.
New algorithm tracks deep RL value functions with uncertainty.
EnSF improves accuracy in tracking high-dimensional nonlinear systems.
New nonlinear smoothers improve state estimation in chaotic systems.
Gaussian Processes (GPs) are powerful kernelized methods for non-parameteric regression used in many applications. However, their use is limited to a few thousand of training samples due to their cubic time complexity. In order to scale GPs to larger datasets, several sparse approximations based on so-called inducing p…
The prediction of the gas production from mature gas wells, due to their complex end-of-life behavior, is challenging and crucial for operational decision making. In this paper, we apply a modified deep LSTM model for prediction of the gas flow rates in mature gas wells, including the uncertainties in input parameters.…
Decentralised optimisation tasks are important components of multi-agent systems. These tasks can be interpreted as n-player potential games: therefore game-theoretic learning algorithms can be used to solve decentralised optimisation tasks. Fictitious play is the canonical example of these algorithms. Nevertheless fic…
Improved Kalman filter for Stiefel manifold measurements.
Controlled interacting particle systems such as the ensemble Kalman filter (EnKF) and the feedback particle filter (FPF) are numerical algorithms to approximate the solution of the nonlinear filtering problem in continuous time. The distinguishing feature of these algorithms is that the Bayesian update step is implemen…
Brain-computer interfaces (BCIs) have enabled prosthetic device control by decoding motor movements from neural activities. Neural signals recorded from cortex exhibit nonstationary property due to abrupt noises and neuroplastic changes in brain activities during motor control. Current state-of-the-art neural signal de…
HKF uses neural networks to adapt Kalman filters for dynamic channel tracking.
Low dimensional representations of words allow accurate NLP models to be trained on limited annotated data. While most representations ignore words' local context, a natural way to induce context-dependent representations is to perform inference in a probabilistic latent-variable sequence model. Given the recent succes…
We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in space and time. We propose a novel filtering methodology that harnesses transportation of measures, convex optimization, and ideas from proba…
The article improves prediction by aggregating Kalman recursions online.
We present an efficient alternating direction method of multipliers (ADMM) algorithm for segmenting a multivariate non-stationary time series with structural breaks into stationary regions. We draw from recent work where the series is assumed to follow a vector autoregressive model within segments and a convex estimati…
In this paper, we revisit the Kalman filter theory. After giving the intuition on a simplified financial markets example, we revisit the maths underlying it. We then show that Kalman filter can be presented in a very different fashion using graphical models. This enables us to establish the connection between Kalman fi…
Illustrates interleaved learning with Kalman Filter for linear least squares.
Given a stationary state-space model that relates a sequence of hidden states and corresponding measurements or observations, Bayesian filtering provides a principled statistical framework for inferring the posterior distribution of the current state given all measurements up to the present time. For example, the Apoll…
In this paper, a Bayesian inference technique based on Taylor series approximation of the logarithm of the likelihood function is presented. The proposed approximation is devised for the case, where the prior distribution belongs to the exponential family of distributions. The logarithm of the likelihood function is li…