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.
Unified approach to equity markets with open and hybrid Jacobi models.
In this work we present a new approach on studying dynamical systems. Combining the two ways of expressing the uncertainty, using probabilistic theory and credibility theory, we have research the generalized fractional hybrid equations. We have introduced the concepts of generalized fractional Wiener process, generaliz…
We introduce a class of hybrid marked point processes, which encompasses and extends continuous-time Markov chains and Hawkes processes. While this flexible class amalgamates such existing processes, it also contains novel processes with complex dynamics. These processes are defined implicitly via their intensity and a…
Paper proposes hybrid machine learning for tuning first principles models in engineering systems.
We study the problem of utility maximization from terminal wealth in which an agent optimally builds her portfolio by investing in a bond and a risky asset. The asset price dynamics follow a diffusion process with regime-switching coefficients modeled by a continuous-time finite-state Markov chain. We consider an inves…
This paper develops a CVaR framework for managing tail risks using puts and trend-following strategies.
A hybrid model for Bayesian optimization handles mixed variables using MCTS for categorical and GP for continuous.
Feedforward computation, such as evaluating a neural network or sampling from an autoregressive model, is ubiquitous in machine learning. The sequential nature of feedforward computation, however, requires a strict order of execution and cannot be easily accelerated with parallel computing. To enable parallelization, w…
We introduce a simulation scheme for Brownian semistationary processes, which is based on discretizing the stochastic integral representation of the process in the time domain. We assume that the kernel function of the process is regularly varying at zero. The novel feature of the scheme is to approximate the kernel fu…
New hybrid RL algorithm outperforms model-free and model-based methods.
Hybrid model improves forest growth predictions.
The hybrid clustering-classification neural network is proposed. This network allows increasing a quality of information processing under the condition of overlapping classes due to the rational choice of a learning rate parameter and introducing a special procedure of fuzzy reasoning in the clustering process, which o…
Hybrid Bayesian neural networks use function uncertainty for probabilistic inference.
This paper constructs Brownian motion on complex flag manifolds and finds joint distribution of stochastic areas.
Hybrid quantum neural networks predict continuous variables.
AI learns to design chemical processes efficiently.
Deep neural nets approximate high-dimensional HJB equations efficiently.
Paper proposes a fast data-driven AC-OPF method using sparse hybrid Gaussian processes.
Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. We consider ballistic firing as an illustrative case study for a Bayesian …
Hybrid model improves music source separation by 1.4 dB.
We develop and study stability properties of a hybrid approximation of functionals of the Bates jump model with stochastic interest rate that uses a tree method in the direction of the volatility and the interest rate and a finite-difference approach in order to handle the underlying asset price process. We also propos…
Dirichlet process mixture models (DPMM) are a cornerstone of Bayesian non-parametrics. While these models free from choosing the number of components a-priori, computationally attractive variational inference often reintroduces the need to do so, via a truncation on the variational distribution. In this paper we presen…
We study twisted Jacobi manifolds, a concept that we had introduced in a previous Note. Twisted Jacobi manifolds can be characterized using twisted Dirac-Jacobi, which are sub-bundles of Courant-Jacobi algebroids. We show that each twisted Jacobi manifold has an associated Lie algebroid with a 1-cocycle. We introduce t…
A model optimizes carbon emission reduction and allowance purchasing for companies.
DiPhon generates scalable graphs via diffusion on graphons.
Study optimal investment strategies for an insurer in two currency markets.
Hybrid models combine domain knowledge and data-driven learning for Earth observation.
We propose a definition of Jacobi quasi-Nijenhuis algebroid and show that any such Jacobi algebroid has an associated quasi-Jacobi bialgebroid. Therefore, also an associated Courant-Jacobi algebroid is obtained. We introduce the notions of quasi-Jacobi bialgebroid morphism and Courant-Jacobi algebroid morphism providin…
BITS for GAPS uses Bayesian methods to improve surrogate model accuracy in complex systems.
Hybrid method improves mutual information estimation from samples.
Hybrid LSMC-PDE method for Bermudan options under GDMR model.
This paper optimizes DC pension plan investments using O-U process and loan.
Hybrid QNN-LSTM predicts financial stock market trends using quantum computing.
Defines Jacobi-Koszul-Vinberg structures on Jacobi-left-symmetric algebroids.
A new method uses ABC-SMC to infer hybrid models in bioprocesses with limited data.
Optimizes control of hybrid systems with multiple switching processes.
The study extends Jacobi-orthogonality to indefinite scalar product spaces.
Paper solves investment strategy optimization with deep learning.
We study quasi-Jacobi and Jacobi-quasi bialgebroids and their relationships with twisted Jacobi and quasi Jacobi manifolds. We show that we can construct quasi-Lie bialgebroids from quasi-Jacobi bialgebroids, and conversely, and also that the structures induced on their base manifolds are related via a quasi Poissoniza…
Many applications in different domains produce large amount of time series data. Making accurate forecasting is critical for many decision makers. Various time series forecasting methods exist which use linear and nonlinear models separately or combination of both. Studies show that combining of linear and nonlinear mo…
Deep models improve spatial and spatio-temporal data analysis.
Bernstein processes are Brownian diffusions that appear in Euclidean Quantum Mechanics. Knowledge of the symmetries of the Hamilton-Jacobi-Bellman equation associated with these processes allows one to obtain relations between stochastic processes (Lescot-Zambrini, Progress in Probability, vols 58 and 59). More recentl…
Paper improves SVaR estimation for stress testing under macro scenarios using a hybrid GPR-HS framework.
In this paper, we study the concept of Parisian ruin under the hybrid observation scheme model introduced by Li et al. \cite{binetal2016}. Under this model, the process is observed at Poisson arrival times whenever the business is financially healthy and it is continuously observed when it goes below . The Parisian …
Defines Dirac pairs on Jacobi algebroids, generalizing Lie algebroids.
We use the supergeometric formalism, more precisely, the so-called "big bracket" (for which brackets and anchors are encoded by functions on some graded symplectic manifold) to address the theory of Jacobi algebroids and bialgebroids (following mainly Iglesias-Marrero and Grabowski-Marmo as a guideline). This formalism…
In this paper, we develop holomorphic Jacobi structures. Holomorphic Jacobi manifolds are in one-to-one correspondence with certain homogeneous holomorphic Poisson manifolds. Furthermore, holomorphic Poisson manifolds can be looked at as special cases of holomorphic Jacobi manifolds. We show that holomorphic Jacobi str…
A hybrid machine learning and process-based-modeling (PBM) approach is proposed and evaluated at a handful of AmeriFlux sites to simulate the top-layer soil moisture state. The Hybrid-PBM (HPBM) employed here uses the Noah land-surface model integrated with Gaussian Processes. It is designed to correct the model only i…