arXiv research

New method uses randomised signatures for generating financial time series data.

Randomised classifiers outperform deterministic ones in strategic classification.

Develops a kernel-based framework for dynamic trading strategies.

New framework for policy gradient methods in continuous time reinforcement learning.

We extend previous large deviations results for the randomised Heston model to the case of moderate deviations. The proofs involve the Gärtner-Ellis theorem and sharp large deviations tools.

New algorithm improves game learning with randomised optimism.

We propose a randomised version of the Heston model-a widely used stochastic volatility model in mathematical finance-assuming that the starting point of the variance process is a random variable. In such a system, we study the small-and large-time behaviours of the implied volatility, and show that the proposed random…

Randomized exploration in linear bandits achieves optimal regret bounds.

Improved Bayesian optimisation method using randomised Gaussian process UCB.

We introduce a general learning framework for private machine learning based on randomised response. Our assumption is that all actors are potentially adversarial and as such we trust only to release a single noisy version of an individual's datapoint. We discuss a general approach that forms a consistent way to estima…

Two modified tests improve the reliability of evaluating explanation methods.

Numerous kinds of uncertainties may affect an economy, e.g. economic, political, and environmental ones. We model the aggregate impact by the uncertainties on an economy and its associated financial market by randomised mixtures of Lévy processes. We assume that market participants observe the randomised mixtures only …

We design a randomised parallel version of Adaboost based on previous studies on parallel coordinate descent. The algorithm uses the fact that the logarithm of the exponential loss is a function with coordinate-wise Lipschitz continuous gradient, in order to define the step lengths. We provide the proof of convergence …

Unified high-probability regret bounds for online convex optimisation with randomised gradient estimators.

New method improves calibration of BayesCG for better uncertainty quantification.

Discrete time analogues of ergodic stochastic differential equations (SDEs) are one of the most popular and flexible tools for sampling high-dimensional probability measures. Non-asymptotic analysis in the $L^2$ Wasserstein distance of sampling algorithms based on Euler discretisations of SDEs has been recently develop…

Improved control approach for correlated bandits with better performance.

We introduce Bayesian least-squares policy iteration (BLSPI), an off-policy, model-free, policy iteration algorithm that uses the Bayesian least-squares temporal-difference (BLSTD) learning algorithm to evaluate policies. An online variant of BLSPI has been also proposed, called randomised BLSPI (RBLSPI), that improves…

New method optimizes experimental design for specific applications.

We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to spheres rather than distance to instances. The randomised nature of RSC makes it ideal for use in en…

We develop a new Monte Carlo variance reduction method to estimate the expectation of two commonly encountered path-dependent functionals: first-passage times and occupation times of sets. The method is based on a recursive approximation of the first-passage time probability and expected occupation time of sets of a Le…

EVILL uses randomised perturbations to improve exploration in bandit problems.

Paper develops a privacy-preserving nonparametric regression method.

Novel strategy for federated learning with privacy-preserving predictors and nonvacuous generalization bounds.

FP uses random projections to train networks without feedback, achieving comparable performance to backpropagation.

The paper revisits expected signatures in semimartingale models, providing new formulae and simplifying complexity.

Study proves value of non-Markovian games with partial, asymmetric info.

Defines knot signature invariant using G-signature theorem.

Deep signature/log-signature FBSDE algorithm improves accuracy and training time.

A well-known property of the signature of closed oriented 4n-dimensional manifolds is Novikov additivity, which states that if a manifold is split into two manifolds with boundary along an oriented smooth hypersurface, then the signature of the original manifold equals the sum of the signatures of the resulting manifol…

Maximum Levine-Tristram signature of torus knots follows a reduction formula.

New findings on mesh group-planes validate Signature-inverse Theorem under specific conditions.

Introduces flat discrete signatures for financial data analysis.

Paper improves privacy bounds for shuffle model using novel numerical techniques.

This is a sequel to the paper "The signature package on Witt spaces, I. Index classes" by the same authors. In the first part we investigated, via a parametrix construction, the regularity properties of the signature operator on a stratified Witt pseudomanifold, proving, in particular, that one can define a K-homology …

We define the Analytical signature, the Hodge signature and the de Rham signature for a foliated manifold with boundary with foliation transverse to the boundary. We show that all these signatures coincide and a Hirzebruch formula is valid.

New methods price American options in rough volatility models.

The paper examines the consistency of Lasso regression applied to signature analysis of time series data.

Study signatures of torus links and their cores using Neumann's equivariant signatures and Hirzebruch's formula.

This paper extends the C*-signature to non-Witt spaces using noncommutative geometric methods.

Paper generalizes path signature using fractional calculus for improved machine learning.

We present a novel method for extracting cancer signatures by applying statistical risk models (http://ssrn.com/abstract=2732453) from quantitative finance to cancer genome data. Using 1389 whole genome sequenced samples from 14 cancers, we identify an "overall" mode of somatic mutational noise. We give a prescription …

pySigLib speeds up signature-based computations on CPUs and GPUs.

Efficiently computes sparse signature coefficients using kernels.

New method calculates signatures of biquotients.

Paper introduces branched signature model for efficient computation and data-driven applications.

Link signature limit depends on linking matrix under specific polynomial condition.

Path signatures improve hedging of exotic derivatives in non-Markovian models.