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 method detects text changes under dependencies, outperforming baselines.
Revisits SWIFT method for option pricing using Shannon wavelets.
SWIFT method speeds up Heston model calibration for European options.
SWIFT learns intrinsic rewards from LLM hidden states for efficient best-of-N sampling.
SWIFT improves time series forecasting on edge devices with wavelet decomposition.
To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift/BAT triggering algorithm for long GRBs, a computationally expensive proce…
Swift Hydra uses RL and generative AI to improve anomaly detection.
Proves existence and uniqueness of solutions for a nonlinear equation on Hilbert manifold.
Swift-Sarsa combines TD learning with Sarsa to control tasks robustly.
Embed-KCPD segments text without labels, outperforming baselines.
Kernelized Taylor diagram visualizes data populations with fewer assumptions.
The present chapter gives an overview on results for discrete knot energies. These discrete energies are designed to make swift numerical computations and thus open the field to computational methods. Additionally, they provide an independent, geometrically pleasing and consistent discrete model that behaves similarly …
Taylor expansions improve reinforcement learning policies.
We propose \emph{Taylorized training} as an initiative towards better understanding neural network training at finite width. Taylorized training involves training the -th order Taylor expansion of the neural network at initialization, and is a principled extension of linearized training---a recently proposed theory …
Study finds Deep Taylor Decomposition is unreliable for explaining neural networks.
The first aperiodic monotiling, introduced by Taylor, was based on a trapezoidal prototile equipped with 14 distinct decorations. A presentation of the closely related Taylor-Socolar aperiodic monotiling is based on a hexagonal prototile equipped with 7 decorations. This paper gives decoration-free algebraic descriptio…
The paper is concerned with non-linear Gaussian filtering and smoothing in continuous-discrete state-space models, where the dynamic model is formulated as an Itô stochastic differential equation (SDE), and the measurements are obtained at discrete time instants. We propose novel Taylor moment expansion (TME) Gaussian …
Proposes a Taylor framework to unify and analyze attribution methods.
New approximations for Asian basket spread options using stochastic Taylor expansions.
We propose a numerical algorithm for backward stochastic differential equations based on time discretization and trigonometric wavelets. This method combines the effectiveness of Fourier-based methods and the simplicity of a wavelet-based formula, resulting in an algorithm that is both accurate and easy to implement. F…
Paper develops a new algorithm to find shortest paths on surfaces.
TaylorPODA uses Taylor expansions to improve feature attributions for opaque models.
Developed Taylor series for muscle-finger system analysis.
The note evaluates different methods for option pricing using Shannon Wavelets.
Modern convolutional networks, incorporating rectifiers and max-pooling, are neither smooth nor convex; standard guarantees therefore do not apply. Nevertheless, methods from convex optimization such as gradient descent and Adam are widely used as building blocks for deep learning algorithms. This paper provides the fi…
The Adomian decomposition method is shown to be equivalent to the Taylor series approach.
Proves a special case of the Gaussian kinematic formula using large sphere limits.
Unified framework for analyzing machine learning model attributions.
We introduce Taylor expansions that do not require the differentiability. We also provide new solutions to partial differential equations. We apply our methods to finance.
An expanding literature articulates the view that Taylor rules are helpful in predicting exchange rates. In a changing world however, Taylor rule parameters may be subject to structural instabilities, for example during the Global Financial Crisis. This paper forecasts exchange rates using such Taylor rules with Time V…
In this work we consider the Taylor expansion of the exponential map of a submanifold immersed in R^n up to order three, in order to introduce the concepts of lateral and frontal deviation. We compute the directions of extreme lateral and frontal deviation for surfaces in R^3. Also we compute, by using the Taylor expan…
New resonance theory for Anosov flows connects spectral properties to mixing measures.
New sampling scheme improves ML accuracy in physics simulations.
We provide new exact Taylor's series with fixed coefficients and without the remainder. We demonstrate the usefulness of this contribution by using it to obtain very simple solutions to (non-linear) PDEs. We also apply the method to the portfolio model.
In this paper we study the Taylor series of an operator-valued function related to the differential of the exponential map. For a smooth manifold with a torsion-free affine connection the operator acting on the space is defined to be the composition of the differential …
Examines how central bank policies affect stock markets and asset prices.
This technical report constructs a theoretical framework to relate standard Taylor approximation based optimisation methods with Natural Gradient (NG), a method which is Fisher efficient with probabilistic models. Such a framework will be shown to also provide mathematical justification to combine higher order methods …
We apply results of Malliavin-Thalmaier-Watanabe for strong and weak Taylor expansions of solutions of perturbed stochastic differential equations (SDEs). In particular, we work out weight expressions for the Taylor coefficients of the expansion. The results are applied to LIBOR market models in order to deal with the …
The paper modifies asset pricing models using Taylor series expansions and market-based averages.
In this paper we derive estimates to the free boundary problem for the Euler equation with surface tension, and without surface tension provided the Rayleigh-Taylor sign condition holds. We prove that as the surface tension tends to zero, when the Rayleigh-Taylor condition is satisfied, solutions converge to the Euler …
In this paper we propose a closed-form approximation for the price of basket options under a multivariate Black-Scholes model, based on Taylor expansions and the calculation of mixed exponential-power moments of a Gaussian distribution. Our numerical results show that a second order expansion provides accurate prices o…
We describe Taylor towers for spaces of knots arising from Goodwillie-Weiss calculus of the embedding functor and extend the configuration space integrals of Bott and Taubes from spaces of knots to the stages of the towers. We show that certain combinations of integrals, indexed by trivalent diagrams, yield cohomology …
New learning algorithm for real analytic functions without gradient descent.
The paper calculates Bachelier option prices using Taylor expansions and applies it as a variance reduction technique.
SOAR improves deep networks' robustness against adversarial examples.
Let M and N be smooth manifolds. For an open V of M let emb(V,N) be the space of embeddings from V to N. By results of Goodwillie and Goodwillie-Klein, the cofunctor V |--> emb(V,N) is analytic if dim(N)-dim(M) > 2. We deduce that its Taylor series converges to it. For details about the Taylor series, see Part I.
Taylor's law of temporal fluctuation scaling, variance mean, is ubiquitous in natural and social sciences. We report for the first time convincing evidence of a solid temporal fluctuation scaling law in stock illiquidity by investigating the mean-variance relationship of the high-frequency illiquidity o…
We consider closed-form approximations for European put option prices within the Heston and GARCH diffusion stochastic volatility models with time-dependent parameters. Our methodology involves writing the put option price as an expectation of a Black-Scholes formula and performing a second-order Taylor expansion aroun…