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.
New issue found in value-based reinforcement learning for stochastic environments.
We improve optimization for data with varying variance.
New method improves parameter estimation in complex stochastic models.
Based on ideas of Pigolla and Setti \cite{PS} we prove that immersed submanifolds with bounded mean curvature of Cartan-Hadamard manifolds are Feller. We also consider Riemannian submersions with compact minimal fibers, and based on various criteria for parabolicity and stochastic completeness, see \c…
We extend Kirman's model by introducing variable event time scale. The proposed flexible time scale is equivalent to the variable trading activity observed in financial markets. Stochastic version of the extended Kirman's agent based model is compared to the non-linear stochastic models of long-range memory in financia…
Generative models for complex stochastic dynamics using adversarial learning.
New algorithms accelerate model-based optimization for stochastic problems.
Stochastic gradient descent procedures have gained popularity for parameter estimation from large data sets. However, their statistical properties are not well understood, in theory. And in practice, avoiding numerical instability requires careful tuning of key parameters. Here, we introduce implicit stochastic gradien…
Stochastic LWTA networks resist adversarial attacks while maintaining accuracy.
Regularized SB process speeds up generative modeling.
We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in mixed-membership stochastic blockmodels (MMSB). Our algorithm is based on the stochastic gradient Riemannian Langevin sampler and achieves both faster speed and higher accuracy at every iteration than the current sta…
Along with developing of Peaceman-Rachford Splittling Method (PRSM), many batch algorithms based on it have been studied very deeply. But almost no algorithm focused on the performance of stochastic version of PRSM. In this paper, we propose a new stochastic algorithm based on PRSM, prove its convergence rate in ergodi…
Novel Fourier-based estimator reveals stochastic leverage effect in high-frequency data.
We study primal-dual type stochastic optimization algorithms with non-uniform sampling. Our main theoretical contribution in this paper is to present a convergence analysis of Stochastic Primal Dual Coordinate (SPDC) Method with arbitrary sampling. Based on this theoretical framework, we propose Optimality Violation-ba…
Study shows how market firm capitalization models converge to stochastic PDE solutions.
Proposes VSGD optimizer combining probabilistic and gradient-based methods.
The article reviews how to set stochastic volatility model parameters.
New EI strategies using OWA and SSD for excess return.
A new method for high-dimensional RBDO using stochastic emulators.
Stochastic Q-learning tackles large action spaces with reduced computation.
We present new stochastic differential equations, that are more general and simpler than the existing Ito-based stochastic differential equations. As an example, we apply our approach to the investment (portfolio) model.
Analyzes non-Markovian environments in stochastic approximation.
Study exchange option pricing with stochastic volatility and correlation.
Deep learning solves complex stochastic control with jumps.
New algorithm solves stochastic optimization problems with unknown gradients.
We develop a general term structure framework taking stochastic discontinuities explicitly into account. Stochastic discontinuities are a key feature in interest rate markets, as for example the jumps of the term structures in correspondence to monetary policy meetings of the ECB show. We provide a general analysis of …
An algorithm is proposed for solving stochastic and finite sum minimization problems. Based on a trust region methodology, the algorithm employs normalized steps, at least as long as the norms of the stochastic gradient estimates are within a specified interval. The complete algorithm---which dynamically chooses whethe…
We show that stochastic interpolation flow maps are Lipschitz with a sharp constant.
GenFormer uses deep learning to generate complex stochastic data.
Unified analysis of stochastic ADMM variants via SME.
Stochastic models analyze traffic network performance.
SVD-based methods reduce computational cost for stochastic systems.
Estimates and optimizes UBSR risk in recursive settings.
New framework for Bayesian inference using neural Schrödinger-Föllmer flows.
We introduce a novel stochastic version of the non-reversible, rejection-free Bouncy Particle Sampler (BPS), a Markov process whose sample trajectories are piecewise linear. The algorithm is based on simulating first arrival times in a doubly stochastic Poisson process using the thinning method, and allows efficient sa…
New method optimizes sensor placement for stochastic systems efficiently.
We study local complexity measures for stochastic convex optimization problems, providing a local minimax theory analogous to that of Hájek and Le Cam for classical statistical problems. We give complementary optimality results, developing fully online methods that adaptively achieve optimal convergence guarantees. Our…
This paper presents a stochastic behavior analysis of a kernel-based stochastic restricted-gradient descent method. The restricted gradient gives a steepest ascent direction within the so-called dictionary subspace. The analysis provides the transient and steady state performance in the mean squared error criterion. It…
Study on selecting between base algorithms in stochastic bandit problems.
A machine learning framework predicts self-induced stochastic resonance in neurons.
A new method improves stochastic gradient descent for faster and more efficient estimation.
The paper generalizes the construction by stochastic flows of consistent utility processes introduced by M. Mrad and N. El Karoui in (2010). The utilities random fields are defined from a general class of processes denoted by $\GX$. Making minimal assumptions and convex constraints on test-processes, we construct by co…
The paper tackles biases in session-based recommender systems by modeling user interest as a stochastic process.
We propose a new generic type of stochastic neurons, called -neurons, that considers activation functions based on Jackson's -derivatives with stochastic parameters . Our generalization of neural network architectures with -neurons is shown to be both scalable and very easy to implement. We demonstrate expe…
We derive properties of the cdf of random variables defined as saddle-type points of real valued continuous stochastic processes. This facilitates the derivation of the first-order asymptotic properties of tests for stochastic spanning given some stochastic dominance relation. We define the concept of Markowitz stochas…
In this work we introduce a conditional accelerated lazy stochastic gradient descent algorithm with optimal number of calls to a stochastic first-order oracle and convergence rate improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of Hazan an…
Stochastic gradient descent on manifolds improves low-rank approximation.
In this paper, we study a family of non-convex and possibly non-smooth inf-projection minimization problems, where the target objective function is equal to minimization of a joint function over another variable. This problem include difference of convex (DC) functions and a family of bi-convex functions as special cas…