arXiv research

The paper models asset pricing in a partially observed market using mean field game theory and exponential quadratic Gaussian framework.

Bayesian investor learns unknown asset drift, trades mean-variance optimal portfolio, but policy is robust to observation model distortion.

EnKBS smoothes complex systems with future observations for causal inference.

Proposes variational Gaussian approximations for solving the Kushner equation.

Study finds non-monotonic Value of Information in dynamic multi-market monopoly.

We consider a model of optimal investment and consumption with both habit formation and partial observations in incomplete Itô processes market. The investor chooses his consumption under the addictive habits constraint while only observing the market stock prices but not the instantaneous rate of return. Applying the …

Online DEM improves tracking of latent states in dynamic systems.

Modeling market dynamics with informed and uninformed traders and fads.

Study on inventory management under uncertainty using smooth ambiguity preference.

Develops asset pricing models with mean field game theory for heterogeneous agents.

The mean-variance hedging (MVH) problem is studied in a partially observable market where the drift processes can only be inferred through the observation of asset or index processes. Although most of the literatures treat the MVH problem by the duality method, here we study a system consisting of three BSDEs derived b…

This paper focuses on spectral filters on graphs, namely filters defined as elementwise multiplication in the frequency domain of a graph. In many graph signal processing settings, it is important to transfer a filter from one graph to another. One example is in graph convolutional neural networks (ConvNets), where the…

A new SOHP filter improves trend estimation in economic time series.

Deep density methods improve filtering in high-dimensional systems.

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…

Gradient filters track moving parameters under noisy data and misspecification.

We simplify Bayesian filtering by framing it as optimization, making it practical for high-dimensional systems.

Develops an inverse particle filter for cognitive systems.

Convolutional neural networks (CNNs) achieve state-of-the-art performance in a wide variety of tasks in computer vision. However, interpreting CNNs still remains a challenge. This is mainly due to the large number of parameters in these networks. Here, we investigate the role of compression and particularly pruning fil…

A novel method reduces dimensionality for filtering SRNs with observed variables.

Kernel learning FBSDE filter improves nonlinear filtering efficiency.

New method filters large networks from financial data to reveal key subnetworks.

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…

Paper proves convergence of Kalman filter on Stiefel manifolds with measurement errors.

Improved Kalman filter for non-linear, non-Gaussian data.

This work analyzes the stability of graph filters under large perturbations.

Advances deep network embedding through multi-filtering GCN.

This work preserves linear invariants in ensemble filters for non-Gaussian data assimilation.

Convolutional Bayesian filtering generalizes state estimation by incorporating inequality conditions.

EnSF improves accuracy in tracking high-dimensional nonlinear systems.

Improved Kalman filter for Stiefel manifold measurements.

Latent FxLMS accelerates ANC by adapting along low-dimensional filter weights.

In this paper we introduce a projection method for the space of probability distributions based on the differential geometric approach to statistics. This method is based on a direct L2 metric as opposed to the usual Hellinger distance and the related Fisher Information metric. We explain how this apparatus can be used…

Robust Kalman filtering method for outlier detection.

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…

This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in on…

Develops inverse unscented Kalman filter for non-linear systems.

New sampling-based approach for filtering problems using multiplicative Gaussian functions.

Filtering data with a pre-trained model improves multimodal contrastive learning performance.

A new filter reduces density fitting to a linear solve, improving performance on nonlinear systems.

In an effort to understand the meaning of the intermediate representations captured by deep networks, recent papers have tried to associate specific semantic concepts to individual neural network filter responses, where interesting correlations are often found, largely by focusing on extremal filter responses. In this …

HKF uses neural networks to adapt Kalman filters for dynamic channel tracking.

Triangular, overlapping Mel-scaled filters ("f-banks") are the current standard input for acoustic models that exploit their input's time-frequency geometry, because they provide a psycho-acoustically motivated time-frequency geometry for a speech signal. F-bank coefficients are provably robust to small deformations in…

A new ML-based filter improves data assimilation for nonlinear systems.

Adaptive Heston model calibration using PCRLB and switching filters.

NBF combines deep learning with classical filtering for better belief tracking.

Filtered conformal ellipsoids for graph-native time series

The paper develops a computational method for efficient online filtering of diffusion processes.