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.
We simplify Bayesian filtering by framing it as optimization, making it practical for high-dimensional systems.
We analyze errors in filtering algorithms using optimal transport.
Develops optimal low-dimensional approximations to high-dimensional SDEs.
Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to determine a function that models the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters,, with the following outcomes: (1) we clarify what guarantees can and cannot be as…
Paper uses optimal transport for Bayesian filtering, deriving new EnKF and FPF formulations.
Online convex optimization is a sequential prediction framework with the goal to track and adapt to the environment through evaluating proper convex loss functions. We study efficient particle filtering methods from the perspective of such a framework. We formulate an efficient particle filtering methods for the non-st…
We consider the nonlinear Kalman filtering problem using Kullback-Leibler (KL) and -divergence measures as optimization criteria. Unlike linear Kalman filters, nonlinear Kalman filters do not have closed form Gaussian posteriors because of a lack of conjugacy due to the nonlinearity in the likelihood. In this paper …
A new ensemble filter uses transport maps and MMD optimization for high-dimensional data assimilation.
FGD reduces noisy gradient variance in SGD for neural networks.
We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i.e. filtering. The algorithm is derived from the variational objective in the filtering setting and cons…
A method for optimal Bayesian filtering using progressive particle flow and optimal transport maps.
Recursive KalmanNet combines neural networks with Kalman filters for precise state estimation.
We examine some differential geometric approaches to finding approximate solutions to the continuous time nonlinear filtering problem. Our primary focus is a new projection method for the optimal filter infinite dimensional Stochastic Partial Differential Equation (SPDE), based on the direct L2 metric and on a family o…
EnEMF uses Epanechnikov kernel for high-dimensional filtering, improving accuracy and robustness.
The process of dynamic state estimation (filtering) based on point process observations is in general intractable. Numerical sampling techniques are often practically useful, but lead to limited conceptual insight about optimal encoding/decoding strategies, which are of significant relevance to Computational Neuroscien…
Optimal Bayesian feature filtering (OBF) is a supervised screening method designed for biomarker discovery. In this article, we prove two major theoretical properties of OBF. First, optimal Bayesian feature selection under a general family of Bayesian models reduces to filtering if and only if the underlying Bayesian m…
Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems. However, significant time and effort is frequently required to tune various Kalman filter model parameters, e.g. process noise covariance, pre-whitening filter models f…
Differentiable PF via entropy-regularized OT for better inference.
The sophisticated structure of Convolutional Neural Network (CNN) allows for outstanding performance, but at the cost of intensive computation. As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction. Although ex…
A new SOHP filter improves trend estimation in economic time series.
Matched filters reveal optimal normalization methods for different market participants.
Illustrates interleaved learning with Kalman Filter for linear least squares.
We revisit the development of grid based recursive approximate filtering of general Markov processes in discrete time, partially observed in conditionally Gaussian noise. The grid based filters considered rely on two types of state quantization: The \textit{Markovian} type and the \textit{marginal} type. We propose a s…
The optimal predictor for a linear dynamical system (with hidden state and Gaussian noise) takes the form of an autoregressive linear filter, namely the Kalman filter. However, a fundamental problem in reinforcement learning and control theory is to make optimal predictions in an unknown dynamical system. To this end, …
Improved robustness for high-dimensional Kalman filtering.
ED-Filter improves eating disorder classification on Twitter.
BALLET filters a high-confidence region of interest for Bayesian optimization.
Optimizes costs in uncertain Markov systems using risk filters.
A new Bayesian filtering method speeds up stochastic Newton optimization.
MoE-F combines LLMs online for better time-series prediction.
It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to rem…
Safety filter for unknown discrete-time systems with learned models and noise covariance.
The filtering-clustering models, including trend filtering and convex clustering, have become an important source of ideas and modeling tools in machine learning and related fields. The statistical guarantee of optimal solutions in these models has been extensively studied yet the investigations on the computational as…
DOPPLER optimizes DP training with low-pass filtering, improving model accuracy.
Paper uses averaging from many particle filters to approximate posterior predictive distributions.
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…
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…
Proposes CE-BASS for robust Kalman filtering with innovative and additive outliers.
New algorithm converges to optimal filter for predicting linear dynamical systems.
Gradient filters track moving parameters under noisy data and misspecification.
The paper develops a computational method for efficient online filtering of diffusion processes.
Many sensors, such as range, sonar, radar, GPS and visual devices, produce measurements which are contaminated by outliers. This problem can be addressed by using fat-tailed sensor models, which account for the possibility of outliers. Unfortunately, all estimation algorithms belonging to the family of Gaussian filters…
We present a filter correlation based model compression approach for deep convolutional neural networks. Our approach iteratively identifies pairs of filters with the largest pairwise correlations and drops one of the filters from each such pair. However, instead of discarding one of the filters from each such pair naï…
ATPF combines PF and EnKF for better inference in complex systems.
DiSK improves DP optimizers by simplifying Kalman filtering for better performance.
The process of dynamic state estimation (filtering) based on point process observations is in general intractable. Numerical sampling techniques are often practically useful, but lead to limited conceptual insight about optimal encoding/decoding strategies, which are of significant relevance to Computational Neuroscien…
This study improves state estimation for nonlinear systems using conditional normalizing flows.
Develops an inverse particle filter for cognitive systems.