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.
The paper addresses online prediction in marginally stable systems with bounded perturbations.
Algorithm learns dynamics from past observations.
Study improves curvature estimate for stable marginally outer trapped hypersurfaces with a free boundary.
Study learns dynamics of linear systems from multiple short trajectories.
ITF improves DSR but inflates curvature, while marginal likelihood reduces it, affecting QoIs.
Paper reduces sample complexity for bilinear systems identification to nearly constant.
Generative models for complex stochastic dynamics using adversarial learning.
New SVM margin bound improves generalization in machine learning.
In this paper, we deal with the problem of marginalization over and conditioning on two disjoint subsets of the node set of chain graphs (CGs) with the LWF Markov property. For this purpose, we define the class of chain mixed graphs (CMGs) with three types of edges and, for this class, provide a separation criterion un…
Default-ERM shortcut learning persists even without additional information.
Margin trading in which investors purchase shares with money borrowed from brokers is blamed to be a major cause of the 2015 Chinese stock market crash. We propose a cascading failure model and examine how an increase in margin trading increases share price vulnerability. The model is based on a bipartite graph of inve…
In this article we investigate the restrictions imposed by the dominant energy condition (DEC) on the topology and conformal type of \textsl{possibly non-compact} marginally outer-trapped surfaces (thus extending Hawking's classical theorem on the topology of black holes). We first prove that an unbounded, stable margi…
Margin system for margin loans using cash and stock as collateral is considered in this paper, which is the line of defence for brokers against risk associated with margin trading. The conditional probability of negative return is used as risk measure, and a recursive algorithm is proposed to realize this measure under…
An active margin system for margin loans is proposed for Chinese margin lending market, which uses cash and randomly selected stock as collateral. The conditional probability of negative return(CPNR) after a forced sale of securities from under-margined account in a falling market is used to measure the risk faced by t…
We derive integral and sup-estimates for the curvature of stably marginally outer trapped surfaces in a sliced space-time. The estimates bound the shear of a marginally outer trapped surface in terms of the intrinsic and extrinsic curvature of a slice containing the surface. These estimates are well adapted to situatio…
In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. This s…
In this paper, we analyze the finite sample complexity of stochastic system identification using modern tools from machine learning and statistics. An unknown discrete-time linear system evolves over time under Gaussian noise without external inputs. The objective is to recover the system parameters as well as the Kalm…
Study stability of surfaces in spacetimes, proving new estimates and theorems.
In order to protect brokers from customer defaults in a volatile market, an active margin system is proposed for the transactions of margin lending in China. The probability of negative return under the condition that collaterals are liquidated in a falling market is used to measure the risk associated with margin loan…
As discussed in the paper, in a matter-filled spacetime, perhaps with positive cosmological constant, a stable marginally outer trapped 2-sphere must satisfy a certain area inquality. Namely, its area must be bounded above by , where is a lower bound on a natural energy momentum term. In this note we cons…
In a matter-filled spacetime, perhaps with positive cosmological constant, a stable marginally outer trapped 2-sphere must satisfy a certain area inequality. Namely, as discussed in the paper, its area must be bounded above by , where is a lower bound on a natural energy-momentum term. We then consider th…
This note shows surfaces in stable 3D data are bounded by area and diameter.
We investigate the class of -stable Poisson-Kingman random probability measures (RPMs) in the context of Bayesian nonparametric mixture modeling. This is a large class of discrete RPMs which encompasses most of the the popular discrete RPMs used in Bayesian nonparametrics, such as the Dirichlet process, Pitman-Yor p…
New bounds quantify estimation error in kernel-based system identification with unknown hyperparameters.
Proposes a new portfolio optimization method considering reward, dispersion, and asymmetry.
Estimates multiple linear systems on a graph with smoothness constraints.
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its Bethe approximation. We show that there exists a regime of empirical marginals where such Bethe learning will fail. By failure we mean that th…
The aim of this paper is to collect some facts about the blowup of Jang's equation. First, we discuss how to construct solutions that blow up at an outermost MOTS. Second, we exclude the possibility that there are extra blowup surfaces in data sets with non-positive mean curvature. Then we investigate the rate of conve…
MSBM extends SB for multi-marginal trajectory inference.
Efficient algorithm predicts unknown linear systems with long-term memory.
Stability is an important aspect of a classification procedure because unstable predictions can potentially reduce users' trust in a classification system and also harm the reproducibility of scientific conclusions. The major goal of our work is to introduce a novel concept of classification instability, i.e., decision…
New mathematical foundations for stable RKHSs improve system identification.
Gradient descent biases towards stable rank networks for nearly-orthogonal data.
A crucial task in system identification problems is the selection of the most appropriate model class, and is classically addressed resorting to cross-validation or using asymptotic arguments. As recently suggested in the literature, this can be addressed in a Bayesian framework, where model complexity is regulated by …
The paper optimizes portfolios using relative tail risk measures.
The OLS estimator optimally identifies stable linear systems with a finite number of samples.
Bayesian deep neural networks converge to processes with α-stable marginals under infinite variance weights.
This paper is devoted to the quantification and analysis of marginal risk contribution of a given single financial institution i to the risk of a financial system s. Our work expands on the CoVaR concept proposed by Adrian and Brunnermeier as a tool for the measurement of marginal systemic risk contribution. We first g…
Study identifies stable configurations of intertwined threads with repulsive interactions.
3MSBM learns smooth trajectories from multiple snapshots.
The paper develops Kalman filters for unknown systems with sample complexity bounds.
Statistic dynamics of financial systems is investigated, basing on a model of randomly coupled equation system driven by stochastic Langevin force. It is found that in stable regime the noise power spectrum of the system is of 1/f^alpha form, with the exponent alpha=3/2 in case of Hermitian coupling matrices, or slight…
The paper proves stability of certain singularities in integrable systems.
This work extracts stochastic dynamical systems with -stable Lévy noise.
The paper defines marginal tubes and proves their null nature.
Study of supervised learning from multiple non-independent sequences.
Paper improves ISDA margin calculation using LSMC.
Stable deep models learn dynamical systems with formal stability guarantees.