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.
New test for point processes without strong model assumptions.
Determinantal point process have recently been used as models in machine learning and this has raised questions regarding the characterizations of conditional independence. In this paper we investigate characterizations of conditional independence. We describe some conditional independencies through the conditions on t…
We introduce a new class of processes for the evaluation of multivariate equity derivatives. The proposed setting is well suited for the application of the standard copula function theory to processes, rather than variables, and easily enables to enforce the martingale pricing requirement. The martingale condition is i…
Greedy selection works well in a toy model of independent increments.
Develops a test for conditional local independence of counting processes.
This research tackles group fairness in predictive process monitoring by ensuring predictions are independent of sensitive group membership.
Several important families of computational and statistical results in machine learning and randomized algorithms rely on uniform bounds on quadratic forms of random vectors or matrices. Such results include the Johnson-Lindenstrauss (J-L) Lemma, the Restricted Isometry Property (RIP), randomized sketching algorithms, …
Study on Volterra Cox-Ingersoll-Ross process, proving asymptotic independence and ergodicity.
Gaussian processes adapted for Riemannian manifolds using gauge-independent kernels.
A new non parametric approach to the problem of testing the independence of two random process is developed. The test statistic is the Hilbert Schmidt Independence Criterion (HSIC), which was used previously in testing independence for i.i.d pairs of variables. The asymptotic behaviour of HSIC is established when compu…
In this paper we consider a new mathematical extension of the Black-Scholes model in which the stochastic time and stock share price evolution is described by two independent random processes. The parent process is Brownian, and the directing process is inverse to the totally skewed, strictly α-stable process. The subo…
The fractional Poisson process (FPP) is a counting process with independent and identically distributed inter-event times following the Mittag-Leffler distribution. This process is very useful in several fields of applied and theoretical physics including models for anomalous diffusion. Contrary to the well-known Poiss…
New method tests independence with single nonstationary time series.
For a large class of vanilla contingent claims, we establish an explicit Föllmer-Schweizer decomposition when the underlying is a process with independent increments (PII) and an exponential of a PII process. This allows to provide an efficient algorithm for solving the mean variance hedging problem. Applications to mo…
New findings show independent subordination is not relevant for accurate option pricing.
The Freund family of distributions becomes a Riemannian 4-manifold with Fisher information as metric; we derive the induced -geometry, i.e., the -curvature, -Ricci curvature with its eigenvales and eigenvectors, the -scalar curvature etc. We show that the Freund manifold has a positive constant 0-scalar cur…
New approach tackles nonidentifiability in nonlinear blind source separation.
We formulate and analyze a graphical model selection method for inferring the conditional independence graph of a high-dimensional nonstationary Gaussian random process (time series) from a finite-length observation. The observed process samples are assumed uncorrelated over time and having a time-varying marginal dist…
We determine the variance-optimal hedge when the logarithm of the underlying price follows a process with stationary independent increments in discrete or continuous time. Although the general solution to this problem is known as backward recursion or backward stochastic differential equation, we show that for this cla…
Paper optimizes approximating high-dimensional diffusions by independent coordinates.
The paper studies affine models driven by independent Lévy processes and their calibration.
Development of metrics for structural data-generating mechanisms is fundamental in machine learning and the related fields. In this paper, we give a general framework to construct metrics on random nonlinear dynamical systems, defined with the Perron-Frobenius operators in vector-valued reproducing kernel Hilbert space…
Study on network-valued processes with asynchronous updates, proving consistency in community and changepoint estimation.
The multiresolution Gaussian process (GP) has gained increasing attention as a viable approach towards improving the quality of approximations in GPs that scale well to large-scale data. Most of the current constructions assume full independence across resolutions. This assumption simplifies the inference, but it under…
New method aggregates Gaussian experts by detecting conditional independence violations.
Modeling interacting objects with latent Gaussian process ODEs.
New method identifies causal structure in exchangeable data.
Improved Gaussian Process model for predicting trajectories without independence assumption errors.
The paper develops a neural network method for estimating drift functions of diffusion processes from discrete observations.
New method identifies latent sources from nonlinear mixtures without auxiliary variables.
FastKCI speeds up KCI tests for causal inference on large datasets.
Characterizes term structure models driven by Lévy processes.
Paper extends nonparametric regression bounds for dependent -mixing samples.
New framework IIA identifies innovations in general nonlinear vector autoregressive processes.
DDICA separates nonlinear mixed signals robustly.
It is inconceivable how chaotic the world would look to humans, faced with innumerable decisions a day to be made under uncertainty, had they been lacking the capacity to distinguish the relevant from the irrelevant---a capacity which computationally amounts to handling probabilistic independence relations. The highly …
A scalable factorized Gaussian process VAE for faster inference.
Independent component analysis (ICA) has become a standard data analysis technique applied to an array of problems in signal processing and machine learning. This tutorial provides an introduction to ICA based on linear algebra formulating an intuition for ICA from first principles. The goal of this tutorial is to prov…
Levy processes, which have stationary independent increments, are ideal for modelling the various types of noise that can arise in communication channels. If a Levy process admits exponential moments, then there exists a parametric family of measure changes called Esscher transformations. If the parameter is replaced w…
We consider the problem of maximizing expected utility from terminal wealth in models with stochastic factors. Using martingale methods and a conditioning argument, we determine the optimal strategy for power utility under the assumption that the increments of the asset price are independent conditionally on the factor…
Entropy regularized OT test assesses independence between samples.
Unified framework for online LLM watermark detection using e-processes.
New AI-block models for clustering high-dimensional variables based on maxima of random processes.
Combines boosting with Gaussian process and mixed effects models.
We develop and apply an approach for analyzing multi-curve data where each curve is driven by a latent state process. The state at any particular point determines a smooth function, forcing the individual curve to switch from one function to another. Thus each curve follows what we call a switching nonparametric regres…
Scalable Gaussian process models trained with unbiased stochastic ELBO.
In Random Forests, proximity distances are a metric representation of data into decision space. By observing how changes in input map to the movement of instances in this space we are able to determine the independent contribution of each feature to the decision-making process. For binary feature vectors, this process …
A method to select important experts for Gaussian processes to balance computational efficiency and uncertainty quantification.