arXiv research

New findings show independent subordination is not relevant for accurate option pricing.

GOCPD detects change points by maximizing the probability of two independent models.

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…

Geometric pruning rules improve change point detection in multiple time series.

In this paper we study the setting where features are added or change interpretation over time, which has applications in multiple domains such as retail, manufacturing, finance. In particular, we propose an approach to provably determine the time instant from which the new/changed features start becoming relevant with…

Framework for causal discovery from changing data.

Unified framework for disentangled representations using mechanistic independence.

We estimate the rate of change of the best constant in the Sobolev inequality of a Euclidean domain which moves outward. Along the way we prove an inequality which reverses the usual Holder inequality, which may be of independent interest.

We consider the problem of quickest change-point detection in data streams. Classical change-point detection procedures, such as CUSUM, Shiryaev-Roberts and Posterior Probability statistics, are optimal only if the change-point model is known, which is an unrealistic assumption in typical applied problems. Instead we p…

Given a heterogeneous time-series sample, the objective is to find points in time (called change points) where the probability distribution generating the data has changed. The data are assumed to have been generated by arbitrary unknown stationary ergodic distributions. No modelling, independence or mixing assumptions…

We study exponential Levy models with change-point which is a random variable, independent from initial Levy processes. On canonical space with initially enlarged filtration we describe all equivalent martingale measures for change-point model and we give the conditions for the existence of f-divergence minimal equival…

Proposes a model to detect changes in multivariate time series data.

Overlay framework simplifies exotic derivative pricing.

It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that…

Paper introduces new invariant for pairs of immersions.

It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that…

RIMs improve generalization by specializing modular structures.

Detects model changes in machine learning with empirical difference test.

We present a new and easy-to-implement sequential sampling method for CGMY processes with either finite or infinite variation, exploiting the time change representation of the CGMY model and a decomposition of its time change. We find that the time change can be decomposed into two independent components. While the fir…

New algorithm for nonstationary multi-armed bandits with optimal performance.

Graphical models are widely used in scienti fic and engineering research to represent conditional independence structures between random variables. In many controlled experiments, environmental changes or external stimuli can often alter the conditional dependence between the random variables, and potentially produce s…

Given a polyhedral surface, assume that it is prohibited to change the shape and size of any face but it is permissible to change the dihedral angles between the faces. A polyhedral surface is said to be flexible if it is possible to change its shape under the above restrictions. We prove that flexible polyhedral surfa…

Random Forest proximity distances reveal feature contributions in black-box models.

New method detects text changes under dependencies, outperforming baselines.

Deep learning generates efficient change-point detection methods.

New framework TDRL identifies latent causal variables from sequential data.

Locally private methods detect changes in time series data.

Model predicts risk-adjusted returns across various financial markets.

The paper develops methods to accurately locate change points in high-dimensional mean shift models.

The method learns disentangled representations for localized image manipulations.

DS-TS adapts to abrupt and smooth changes in bandit problems.

Generative model for morphological continuum of normal and pathological states.

SGD reduces test error by decorrelating updates.

Novel approach combines local and global brain changes for AD prediction.

In this paper, we show how the sampling properties of the Hurst exponent methods of estimation change with the presence of heavy tails. We run extensive Monte Carlo simulations to find out how rescaled range analysis (R/S), multifractal detrended fluctuation analysis (MF-DFA), detrending moving average (DMA) and genera…

DSE learns transferable skills across changing dynamics and goals.

GLR-klUCB detects change-points in non-stationary bandits efficiently.

Facial attribute editing aims to manipulate single or multiple attributes of a face image, i.e., to generate a new face with desired attributes while preserving other details. Recently, generative adversarial net (GAN) and encoder-decoder architecture are usually incorporated to handle this task with promising results.…

Paper introduces Ddim, a new measure of model complexity, for MDL-based learning and change detection.

Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to d…

A new method detects changes in data sequences by comparing backward and forward confidence sequences.

Paper estimates differences in conditional independence graphs from time-dependent data.

New method detects change-points in population genetics.

A new robust and flexible classification method for non-Gaussian data.

Detects change points in time series focusing on specific components.

Develops a theory of common decomposition for correlated Brownian motions.

Robustly detects and attributes climate change impacts under interventions.

This work introduces robust counterfactuals for neural networks that remain valid after minor model changes.