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.
Proposes a deep neural network for multi-dimensional functional data classification.
Robust deep neural networks estimate multi-dimensional functional data robustly.
Study optimal stopping times for multi-dimensional processes with non-exponential discounting.
New method for handling multi-dimensional singular controls with jump costs in mean-field problems.
A new framework using kernel packets overcomes limitations of state space models for multi-dimensional data.
We consider a zero-sum stochastic differential controller-and-stopper game in which the state process is a controlled diffusion evolving in a multi-dimensional Euclidean space. In this game, the controller affects both the drift and the volatility terms of the state process. Under appropriate conditions, we show that t…
TEAFormers preserve multi-dimensional time series structures for better forecasting.
This paper describes a novel method to approximate the polynomial coefficients of regression functions, with particular interest on multi-dimensional classification. The derivation is simple, and offers a fast, robust classification technique that is resistant to over-fitting.
The paper focuses on the sparse approximation of signals using overcomplete representations, such that it preserves the (prior) structure of multi-dimensional signals. The underlying optimization problem is tackled using a multi-dimensional split Bregman optimization approach. An extensive empirical evaluation shows ho…
A low-rank tensor model simplifies multi-dimensional Markov chains.
MTL improves multi-dimensional regression in luminescence sensing.
New method for valid and exact statistical inference of multi-dimensional change-points.
This paper aims to make a new contribution to the study of lifetime ruin problem by considering investment in two hedge funds with high-watermark fees and drift uncertainty. Due to multi-dimensional performance fees that are charged whenever each fund profit exceeds its historical maximum, the value function is expecte…
New framework for analyzing games with multi-dimensional singular controls and non-linear jumps.
Paper solves robust multi-dimensional scaling with accelerated projections.
The paper defines and solves time-inconsistent stopping control problems in multi-dimensional diffusion models.
Paper defines multi-dimensional fractional Brownian motion under volatility uncertainty.
The paper develops algorithms to restore monotonicity in non-monotone functions.
Paper formalizes multi-dimensional FSD using geometric methods.
Many signals on Cartesian product graphs appear in the real world, such as digital images, sensor observation time series, and movie ratings on Netflix. These signals are "multi-dimensional" and have directional characteristics along each factor graph. However, the existing graph Fourier transform does not distinguish …
Generative model combines multi-dimensional annotations for more accurate ground truth estimation.
A new method estimates multi-dimensional value distributions using Hilbert space embeddings.
We calculate heat invariants of arbitrary Riemannian manifolds without boundary. Every heat invariant is expressed in terms of powers of the Laplacian and the distance function. Our approach is based on a multi-dimensional generalization of the Agmon-Kannai method. An application to computation of the Korteweg-de Vries…
Paper solves multi-dimensional passport option pricing problem using machine learning.
Principal binets generalize curvature line surfaces to square lattices and are a discrete integrable system.
This paper characterizes how randomized neural networks generalize well in multi-dimensional tasks.
This paper proposes and analyses a new multilevel Monte Carlo method for the estimation of mean exit times for multi-dimensional Brownian diffusions, and associated functionals which correspond to solutions to high-dimensional parabolic PDEs through the Feynman-Kac formula. In particular, it is proved that the complexi…
The abstract introduces a new concept called flagfolds to model multi-dimensional shapes.
New method models MTPP without predefined intensity functions.
This paper introduces a probability density estimator based on Green's function identities. A density model is constructed under the sole assumption that the probability density is differentiable. The method is implemented as a binary likelihood estimator for classification purposes, so issues such as mis-modeling and …
We study a method of reducing space dimension in multi-dimensional Black-Scholes partial differential equations as well as in multi-dimensional parabolic equations. We prove that a multiplicative transformation of space variables in the Black-Scholes partial differential equation reserves the form of Black-Scholes part…
We derive deterministic criteria for the existence and non-existence of equivalent (local) martingale measures for financial markets driven by multi-dimensional time-inhomogeneous diffusions. Our conditions can be used to construct financial markets in which the \emph{no unbounded profit with bounded risk} condition ho…
Paper proves stability of multi-dimensional rarefaction waves in gas dynamics.
New framework for data-driven hyperparameter tuning with structured loss.
Contrast uses normalizing flows to create precise prediction regions for multi-dimensional outputs.
Improved crude oil price forecasting using multi-dimensional LLM sentiment signals.
This paper investigates the position (state) distribution of the single step binomial (multi-nomial) process on a discrete state / time grid under the assumption that the velocity process rather than the state process is Markovian. In this model the particle follows a simple multi-step process in velocity space which a…
Proposes a new model for clustering passenger trips considering hierarchical and multi-dimensional data.
Intersectionality is a framework that analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability. Intersectionality theory therefore implies it is important that fairness in artificial intelligence systems b…
Extending Itô's formula to non-smooth functions is important both in theory and applications. One of the fairly general extensions of the formula, known as Meyer-Itô, applies to one dimensional semimartingales and convex functions. There are also satisfactory generalizations of Itô's formula for diffusion processes whe…
In this paper, we address the problem of measuring and analysing sensation, the subjective magnitude of one's experience. We do this in the context of the method of triads: the sensation of the stimulus is evaluated via relative judgments of the form: "Is stimulus S_i more similar to stimulus S_j or to stimulus S_k?". …
We consider the optimization of an uncertain objective over continuous and multi-dimensional decision spaces in problems in which we are only provided with observational data. We propose a novel algorithmic framework that is tractable, asymptotically consistent, and superior to comparable methods on example problems. O…
We introduce a new loss function for evaluating forecasts and estimate models using it.
The first widely used financial model is linked to dynamical Hamilton jacobi model
Probability Density Estimation (PDE) is a multivariate discrimination technique based on sampling signal and background densities defined by event samples from data or Monte-Carlo (MC) simulations in a multi-dimensional phase space. In this paper, we present a modification of the PDE method that uses a self-adapting bi…
We model the price of a stock via a Langévin equation with multi-dimensional fluctuations coupled in the price and in time. We generalize previous models in that we assume that the fluctuations conditioned on the time step are compound Poisson processes with operator stable jump intensities. We derive exact relations f…
In this paper we introduce a new multilevel Monte Carlo (MLMC) estimator for multi-dimensional SDEs driven by Brownian motions. Giles has previously shown that if we combine a numerical approximation with strong order of convergence with MLMC we can reduce the computational complexity to estimate expected value…
New method for time series prediction with uncertainty quantification.