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.
A conjugate Bayesian method detects change points in Hawkes processes efficiently.
Two-step process generates molecules from latent vectors.
We consider evaluation methods for payoffs with an inherent financial risk as encountered for instance for portfolios held by pension funds and insurance companies. Pricing such payoffs in a way consistent to market prices typically involves combining actuarial techniques with methods from mathematical finance. We prop…
New method for insurance valuation combining hedging and risk minimization.
The paper extends two-step homogeneous geodesics to homogeneous Finsler spaces.
Classifies two-step solvable Lie groups with SKT structures.
We consider a method popular in the literature of associating a two-step nilpotent Lie algebra with a finite simple graph. We prove that the two-step nilpotent Lie algebras associated with two graphs are Lie isomorphic if and only if the graphs from which they arise are isomorphic.
This paper merges deterministic policy gradient estimations to improve deep reinforcement learning performance.
New method improves wind and solar energy forecasts by 48 hours.
Paper introduces new actuarial-consistent valuations for insurance liabilities.
We study time-consistency questions for processes of monetary risk measures that depend on bounded discrete-time processes describing the evolution of financial values. The time horizon can be finite or infinite. We call a process of monetary risk measures time-consistent if it assigns to a process of financial values …
A new training method improves stability and generalization of DeepONets.
This paper is devoted to a priori estimates for strictly locally convex radial graphs with prescribed Weingarten curvature and boundary in space forms. By constructing two-step continuity process and applying degree theory arguments, existence results in space forms are established for prescribed Gauss curvature …
Proves conjecture about compatible SKT and balanced metrics on compact solvmanifolds.
We prove that two-step analytic sub-Riemannian structures on a compact analytic manifold equipped with a smooth measure and Lipschitz Carnot groups satisfy measure contraction properties.
A Riemannian Einstein solvmanifold (possibly, any noncompact homogeneous Einstein space) is almost completely determined by the nilradical of its Lie algebra. A nilpotent Lie algebra, which can serve as the nilradical of an Einstein metric solvable Lie algebra, is called an Einstein nilradical. Despite a substantial pr…
A new multivariate stochastic volatility estimation procedure for financial time series is proposed. A Wishart autoregressive process is considered for the volatility precision covariance matrix, for the estimation of which a two step procedure is adopted. The first step is the conditional inference on the autoregressi…
We associate a two-step nilpotent Lie algebra to an arbitrary Schreier graph. We then use properties of the Schreier graph to determine necessary and sufficient conditions for this Lie algebra to extend to a three-step nilpotent Lie algebra. As an application, if we start with pairs of non-isomorphic Schreier graphs co…
This paper proposes an alternative to the classical price-adjustment mechanism (called "tâtonnement" after Walras) that is second-order in time. The proposed mechanism, an analogue to the damped harmonic oscillator, provides a dynamic equilibration process that depends only on local information. We show how such a proc…
A two-step nonparametric method estimates financial systemic risk.
A new two-step LSMC method improves game option pricing accuracy.
In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to detect neural peak activities. Second, inferring the degree of association between neurons from partial correlation …
Two-step conformal prediction method for adaptive bounding box uncertainties in multi-object detection.
It has often been taken as a working assumption that directed links in information networks are frequently formed by "short-cutting" a two-step path between the source and the destination -- a kind of implicit "link copying" analogous to the process of triadic closure in social networks. Despite the role of this assump…
A new two-step MH method for Bayesian EL computation.
The paper discusses a new method for constructing two-step Darboux transforms of isothermic surfaces.
A new Bayesian method optimizes time-dependent expensive functions with lookahead.
New method estimates spatial weights matrix for lattice data, improving prediction accuracy.
Method learns SDEs from data snapshots.
The paper extends risk measures to two-step approximations and studies log-concave distributions.
The paper studies time-optimal problems on specific Lie groups, describing orbits and integrals.
The paper develops a neural network-based method for detecting change points in large-scale time-evolving data.
The paper discusses polynomial convergence to conical Kähler-Einstein metrics.
New methods use vector search and nearest-neighbor matching for policy learning in causal inference.
New method estimates volatility for Lévy processes with unbounded jumps efficiently.
Patch priors have become an important component of image restoration. A powerful approach in this category of restoration algorithms is the popular Expected Patch Log-Likelihood (EPLL) algorithm. EPLL uses a Gaussian mixture model (GMM) prior learned on clean image patches as a way to regularize degraded patches. In th…
DFA trains deep networks by aligning weights then memorizing data.
We introduce a two step algorithm with theoretical guarantees to recover a jointly sparse and low-rank matrix from undersampled measurements of its columns. The algorithm first estimates the row subspace of the matrix using a set of common measurements of the columns. In the second step, the subspace aware recovery of …
In this paper we study the geometry of simply connected two-step nilpotent Lie groups of dimension five. We give the Levi-Civita connection, curvature tensor, sectional and scalar curvatures of these spaces and show that they have constant negative scalar curvature. Also we show that the only space which admits left in…
A new method for accurately reconstructing signals without knowing the kernel or signal regularity.
Proposes a two-step method for sound source separation.
Determinantal point processes (DPPs) enable the modeling of repulsion: they provide diverse sets of points. The repulsion is encoded in a kernel that can be seen as a matrix storing the similarity between points. The diversity comes from the fact that the inclusion probability of a subset is equal to the determinan…
Fault detection in industrial plants is a hot research area as more and more sensor data are being collected throughout the industrial process. Automatic data-driven approaches are widely needed and seen as a promising area of investment. This paper proposes an effective machine learning algorithm to predict industrial…
Bayesian algorithm discovers synthetic routes from target molecules.
New families of non-singular geodesic orbit nilmanifolds discovered.
Surface parameterizations have been widely used in computer graphics and geometry processing. In particular, as simply-connected open surfaces are conformally equivalent to the unit disk, it is desirable to compute the disk conformal parameterizations of the surfaces. In this paper, we propose a novel algorithm for the…
Efficiently optimizes constrained problems with two-step lookahead BO.
GD converges in unstable regimes, even with oscillatory behavior.