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.
Matched filters reveal optimal normalization methods for different market participants.
In this paper, we consider recommender systems with side information in the form of graphs. Existing collaborative filtering algorithms mainly utilize only immediate neighborhood information and have a hard time taking advantage of deeper neighborhoods beyond 1-2 hops. The main caveat of exploiting deeper graph informa…
Method introduces topological regularization using information filtering networks.
This paper studies the question of filtering and maximizing terminal wealth from expected utility in a partially information stochastic volatility models. The special features is that the only information available to the investor is the one generated by the asset prices, and the unobservable processes will be modeled …
The paper develops a filtering framework for estimating hazard rates with jumps in financial and insurance applications.
New algorithm converges to optimal filter for predicting linear dynamical systems.
We cast Amari's natural gradient in statistical learning as a specific case of Kalman filtering. Namely, applying an extended Kalman filter to estimate a fixed unknown parameter of a probabilistic model from a series of observations, is rigorously equivalent to estimating this parameter via an online stochastic natural…
Kähler information manifolds for signal filters in weighted Hardy spaces are explored.
Investigates a Kyle model with imperfect information and risk aversion.
Proposes a nonparametric approach for inferring spike train filters.
EnSF improves accuracy in tracking high-dimensional nonlinear systems.
Develops inverse extended Kalman filter for predicting adversarial steps.
A new ensemble filter uses transport maps and MMD optimization for high-dimensional data assimilation.
Deep neural networks with convolutional layers usually process the entire spectrogram of an audio signal with the same time-frequency resolutions, number of filters, and dimensionality reduction scale. According to the constant-Q transform, good features can be extracted from audio signals if the low frequency bands ar…
Advances in collaborative filtering and ranking methods.
BankGCN improves graph convolution networks by handling multi-channel signals with adaptive filter banks.
The recent discovered spatial-temporal information processing capability of bio-inspired Spiking neural networks (SNN) has enabled some interesting models and applications. However designing large-scale and high-performance model is yet a challenge due to the lack of robust training algorithms. A bio-plausible SNN mode…
The extended Kalman filter is perhaps the most standard tool to estimate in real time the state of a dynamical system from noisy measurements of some function of the system, with extensive practical applications (such as position tracking via GPS). While the plain Kalman filter for linear systems is well-understood, th…
We construct Hodge filtered cohomology groups for complex manifolds that combine the topological information of generalized cohomology theories with geometric data of Hodge filtered holomorphic forms. This theory provides a natural generalization of Deligne cohomology. For smooth complex algebraic varieties, we show th…
The paper proposes a method to detect and filter noisy or mislabeled data using pointwise mutual information.
Paper compares semi-supervised training for differentiable particle filters.
Binary Stochastic Filtering (BSF), the algorithm for feature selection and neuron pruning is proposed in this work. The method defines filtering layer which penalizes amount of the information involved in the training process. This information could be the input data or output of the previous layer, which directly lead…
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…
The ability to track a moving vehicle is of crucial importance in numerous applications. The task has often been approached by the importance sampling technique of particle filters due to its ability to model non-linear and non-Gaussian dynamics, of which a vehicle travelling on a road network is a good example. Partic…
HKF uses neural networks to adapt Kalman filters for dynamic channel tracking.
Improved method using filtered PDEs for robust physics-informed deep learning.
Develops optimal low-dimensional approximations to high-dimensional SDEs.
The paper develops a computational method for efficient online filtering of diffusion processes.
KalmanNet uses neural networks to improve state estimation in systems with unknown dynamics.
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…
Recursive KalmanNet combines neural networks with Kalman filters for precise state estimation.
I-AID categorizes disaster tweets into useful information types.
Proposes a new method to enhance neural learning by maximizing information gain.
Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical…
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…
Graph Kalman filters adapt classical filters to graph data.
The paper studies derivative asset analysis in structural credit risk models where the asset value of the firm is not fully observable. It is shown that in order to compute the price dynamics of traded securities one needs to solve a stochastic filtering problem for the asset value. We transform this problem to a filte…
Collaborative filtering is used to recommend items to a user without requiring a knowledge of the item itself and tends to outperform other techniques. However, collaborative filtering suffers from the cold-start problem, which occurs when an item has not yet been rated or a user has not rated any items. Incorporating …
MoE-F combines LLMs online for better time-series prediction.
Several algorithms have been proposed to filter information on a complete graph of correlations across stocks to build a stock-correlation network. Among them the planar maximally filtered graph (PMFG) algorithm uses edges to build a graph whose features include a high frequency of small cliques and a good clust…
A new variational method for SSMs improves inference efficiency.
We show that the Kullback-Leibler distance is a good measure of the statistical uncertainty of correlation matrices estimated by using a finite set of data. For correlation matrices of multivariate Gaussian variables we analytically determine the expected values of the Kullback-Leibler distance of a sample correlation …
HLOB predicts mid-price changes in L.O.Bs using deep learning.
The problem of filtering information from large correlation matrices is of great importance in many applications. We have recently proposed the use of the Kullback-Leibler distance to measure the performance of filtering algorithms in recovering the underlying correlation matrix when the variables are described by a mu…
We present a provable, sampling-based approach for generating compact Convolutional Neural Networks (CNNs) by identifying and removing redundant filters from an over-parameterized network. Our algorithm uses a small batch of input data points to assign a saliency score to each filter and constructs an importance sampli…
Develops an inverse particle filter for cognitive systems.
Study finds non-monotonic Value of Information in dynamic multi-market monopoly.
WaveletGAN improves GANs by homogenizing noise through multi-channel wavelet filtering.