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.
Novel method for multiclass ROC curves using multidimensional Gini index.
A method to visualize multidimensional local subspaces using implicit differentiation.
Efficiently recovers piecewise linear functions from noisy samples.
A new learning method for prosthetic arms without explicit rewards.
UAPCA projects uncertain data to low dimensions using GMMs.
Extends multidimensional scaling to analyze three-way asymmetric proximities.
Unified NICEk metrics improve solar forecasting accuracy.
A method for multidimensional probabilistic electricity market forecasting is proposed.
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…
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…
Bayesian hyperbolic MDS improves tree-like data representation.
This paper deals with multidimensional dynamic risk measures induced by conditional -expectations. A notion of multidimensional -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…
This paper improves conditional multidimensional scaling for incomplete data.
The paper explores multidimensional critic output in GANs, improving convergence and diversity.
Improved algorithm for multidimensional scaling reduces stress.
The paper presents a machine learning approach to multidimensional item response theory.
Global minima found for multidimensional scaling with penalties.
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…
Study uses multidimensional SE-NBD process to analyze default portfolios and identify shock amplification.
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…
The ability of having a sparse representation for a certain class of signals has many applications in data analysis, image processing, and other research fields. Among sparse representations, the cosparse analysis model has recently gained increasing interest. Many signals exhibit a multidimensional structure, e.g. ima…
Bayesian Complementary Kernelized Learning models complex spatiotemporal data.
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 novel online framework for analyzing multidimensional functional data.
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.
Probabilistic classifiers output a probability distribution on target classes rather than just a class prediction. Besides providing a clear separation of prediction and decision making, the main advantage of probabilistic models is their ability to represent uncertainty about predictions. In safety-critical applicatio…
sWk-means clusters multidimensional financial time series into distinct market regimes.
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…
LR-Robot automates SLRs with AI, expert oversight, and multidimensional analysis.
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.
New method for learning multidimensional CDFs using Archimedean copulas.
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 …
DPI quantifies phase differences in 1D and multidimensional signals using Riesz transform.
Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i.e., sampling is irregular both in time and across dimensions)-such as in the case of clinical patient data. To address these challenges, we propose (1) a continuous-time version of the Gated Recurren…
Develops statistical confidence sets for multidimensional scaling.
Generalizations of the Weierstrass formulae to generic surface immersed into , and into multidimensional Riemann spaces are proposed. Integrable deformations of surfaces in these spaces via the modified Veselov-Novikov equation are discussed.
Optimal B-robust estimate is constructed for multidimensional parameter in drift coefficient of diffusion type process with small noise. Optimal mean-variance robust (optimal V -robust) trading strategy is find to hedge in mean-variance sense the contingent claim in incomplete financial market with arbitrary informatio…
Unified model combines neural networks and dictionary learning for clinical predictions from brain 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 …
In this paper the well-known Dubrovin-Novikov problem posed as long ago as 1984 in connection with the Hamiltonian theory of systems of hydrodynamic type, namely, the classification problem for multidimensional Poisson brackets of hydrodynamic type, is solved. In contrast to the one-dimensional case, in the general cas…
Efficiently analyzes multidimensional functional data using separable basis functions.
We present a novel view of nonlinear manifold learning using derivative-free optimization techniques. Specifically, we propose an extension of the classical multi-dimensional scaling (MDS) method, where instead of performing gradient descent, we sample and evaluate possible "moves" in a sphere of fixed radius for each …
The present contribution suggests the use of a multidimensional scaling (MDS) algorithm as a visualization tool for manifold-valued elements. A visualization tool of this kind is useful in signal processing and machine learning whenever learning/adaptation algorithms insist on high-dimensional parameter manifolds.
Two new feature selection algorithms improve on RFE.
Paper analyzes multidimensional PIDEs for financial modeling, proving existence and uniqueness in Bessel spaces.