arXiv research

Global minima found for multidimensional scaling with penalties.

A novel online framework for analyzing multidimensional functional data.

A method to visualize multidimensional local subspaces using implicit differentiation.

Efficiently analyzes multidimensional functional data using separable basis functions.

Study uses multidimensional SE-NBD process to analyze default portfolios and identify shock amplification.

Efficiently recovers piecewise linear functions from noisy samples.

CovNet models covariance for multidimensional functional data efficiently.

We investigate aspects of semimartingale decompositions, approximation and the martingale representation for multidimensional correlated Markov processes. A new interpretation of the dependence among processes is given using the martingale approach. We show that it is possible to represent, in both continuous and discr…

Paper analyzes multidimensional PIDEs for financial modeling, proving existence and uniqueness in Bessel spaces.

New method for learning multidimensional CDFs using Archimedean copulas.

Adaptive tensor modeling preserves continuity in multidimensional data.

In this paper, we study optimal switching problems under ambiguity. To characterize the optimal switching under ambiguity in the finite horizon, we use multidimensional reflected backward stochastic differential equations (multidimensional RBSDEs) and show that a value function of the optimal switching under ambiguity …

We investigate the use of Malliavin calculus in order to calculate the Greeks of multidimensional complex path-dependent options by simulation. For this purpose, we extend the formulas employed by Montero and Kohatsu-Higa to the multidimensional case. The multidimensional setting shows the convenience of the Malliavin …

This work concerns estimation of multidimensional nonlinear regression models using multilayer perceptron (MLP). The main problem with such model is that we have to know the covariance matrix of the noise to get optimal estimator. however we show that, if we choose as cost function the logarithm of the determinant of t…

New algorithm for decomposing multidimensional, non-stationary signals.

UAPCA projects uncertain data to low dimensions using GMMs.

This paper deals with multidimensional dynamic risk measures induced by conditional $g$-expectations. A notion of multidimensional $g$-expectation is proposed to provide a multidimensional version of nonlinear expectations. By a technical result on explicit expressions for the comparison theorem, uniqueness theorem and…

We provide an asymptotic expansion of the value function of a multidimensional utility maximization problem from consumption with small non-linear price impact. In our model cross-impacts between assets are allowed. In the limit for small price impact, we determine the asymptotic expansion of the value function around …

In this article, we propose an exact simulation method of the Wishart multidimensional stochastic volatility (WMSV) model, which was recently introduced by Da Fonseca et al. \cite{DGT08}. Our method is based onanalysis of the conditional characteristic function of the log-price given volatility level. In particular, we…

This paper improves conditional multidimensional scaling for incomplete data.

A method for multidimensional probabilistic electricity market forecasting is proposed.

The paper explores multidimensional critic output in GANs, improving convergence and diversity.

Improved algorithm for multidimensional scaling reduces stress.

In this article, we consider European options of type $h(X^1_T, X^2_T,\ldots, X^n_T)$ depending on several underlying assets. We study how such options can be valued in terms of simple vanilla options in non-specified market models. We consider different approaches related to static hedging and derive several pricing f…

Exact Gaussian Process (GP) regression has O(N^3) runtime for data size N, making it intractable for large N. Many algorithms for improving GP scaling approximate the covariance with lower rank matrices. Other work has exploited structure inherent in particular covariance functions, including GPs with implied Markov st…

Multidimensional scaling is an important dimension reduction tool in statistics and machine learning. Yet few theoretical results characterizing its statistical performance exist, not to mention any in high dimensions. By considering a unified framework that includes low, moderate and high dimensions, we study multidim…

Paper proves MDS NP-hard and provides a PTAS.

Multidimensional time series are sequences of real valued vectors. They occur in different areas, for example handwritten characters, GPS tracking, and gestures of modern virtual reality motion controllers. Within these areas, a common task is to search for similar time series. Dynamic Time Warping (DTW) is a common di…

Whitney type examples of maps $f\in C^k(\real^m,\real^n)$ for a maximal possible real $k$, and multidimensional space-filling curves with special properties are constructed.

New neural network captures spatial correlations in wind speed predictions.

Novel method for multiclass ROC curves using multidimensional Gini index.

We show that shortfall risks of American options in a sequence of multinomial approximations of the multidimensional Black--Scholes (BS) market converge to the corresponding quantities for similar American options in the multidimensional BS market with path dependent payoffs. In comparison to previous papers we conside…

A method for accurate pricing of multidimensional derivatives under uncertain volatility.

Paper proposes conditional multidimensional scaling for better data reduction.

On the base of Lie algebraic and differential geometry methods, a wide class of multidimensional nonlinear systems is obtained, and the integration scheme for such equations is proposed.

Paper presents adaptive minimax risk classifiers for multidimensional concept drift.

The paper explores the geometry of level lines of quasiperiodic functions with many periods.

sWk-means clusters multidimensional financial time series into distinct market regimes.

ProGO optimizes non-convex functions without gradients, outperforming existing methods.

Extends specific relative entropy to multidimensional continuous martingales.

Classical multidimensional scaling is an important dimension reduction technique. Yet few theoretical results characterizing its statistical performance exist. This paper provides a theoretical framework for analyzing the quality of embedded samples produced by classical multidimensional scaling. This lays the foundati…

In this work, a unified framework for gradient-free Multidimensional Scaling (MDS) based on Coordinate Search (CS) is proposed. This family of algorithms is an instance of General Pattern Search (GPS) methods which avoid the explicit computation of derivatives but instead evaluate the objective function while searching…

Dynamics of four-dimensional massless fields of all spins is formulated in the Siegel space of complex $4\times 4$ symmetric matrices. It is shown that the unfolded equations of free massless fields, that have a form of multidimensional Schrodinger equations, naturally distinguish between positive- and negative-frequen…

Uniform proof for ultradifferentiability in various classes and dimensions.

New method simulates sticky boundaries in multidimensional diffusions.

Extends Carathéodory's theorem to multidimensional domains with constant curvature.

A new tensor regression model preserves multidimensional data structure.

Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance. Different real-world applications evaluate performance by different cost functions of interest. Current LE algorithms often aim to optimize one specific cost function, b…