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.
Paper establishes sufficient condition for comparing linear combinations of infinite-mean risks.
Defines diversification as a binary relationship between financial portfolios.
A meta-UCB method combines stochastic bandit algorithms.
This paper tackles efficient and scalable estimation of a complex model involving stochastic linear combinations of non-linear regressions.
This work connects LLE, factor analysis, and probabilistic PCA through a stochastic perspective.
First-order method solves stochastic bilevel optimization with linear constraints.
The study finds no evidence of stochastic arbitrage opportunities in S&P 500 index options.
This paper considers method of creation of an advisor and indicator based on the spectral stochastic analysis model, both with linear and non-linear approximation. The problem of entrance to one or another trade position is solved on the basis of combined analysis of dynamics of quotations of all currency pairs, what a…
The study analyzes stochastic Lie systems and their applications in various models.
The paper develops a deep signature approach for option pricing under non-Markovian stochastic volatility models.
Proposes a new method combining Reservoir Computing and Normalizing Flow for predicting stochastic dynamical systems.
We introduce the safe linear stochastic bandit framework---a generalization of linear stochastic bandits---where, in each stage, the learner is required to select an arm with an expected reward that is no less than a predetermined (safe) threshold with high probability. We assume that the learner initially has knowledg…
This paper introduces a linear state-space model with time-varying dynamics. The time dependency is obtained by forming the state dynamics matrix as a time-varying linear combination of a set of matrices. The time dependency of the weights in the linear combination is modelled by another linear Gaussian dynamical model…
Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (in the number of agents) relative to non-cooperative approaches in the strongly-convex setting. More re…
Push-SAGA is a decentralized algorithm for directed graphs that converges linearly.
Proposes new stochastic algorithms for multi-objective optimization.
Stochastic gradient methods enable learning probabilistic models from large amounts of data. While large step-sizes (learning rates) have shown to be best for least-squares (e.g., Gaussian noise) once combined with parameter averaging, these are not leading to convergent algorithms in general. In this paper, we conside…
The paper develops robust tests for detecting independence in synchronous stochastic systems with finite sample guarantees.
New method for linear bandits with unknown sparsity, improving sparse regret bounds.
Real-world problems of operations research are typically high-dimensional and combinatorial. Linear programs are generally used to formulate and efficiently solve these large decision problems. However, in multi-period decision problems, we must often compute expected downstream values corresponding to current decision…
New algorithm tackles batched stochastic linear bandits with 1-bit communication constraints.
Stochastic gradient descent (SGD) is a well known method for regression and classification tasks. However, it is an inherently sequential algorithm at each step, the processing of the current example depends on the parameters learned from the previous examples. Prior approaches to parallelizing linear learners using SG…
Method predicts future rewards from past actions in a linear Gaussian system.
BAVART model combines VAR and BART for non-linear forecasting.
In this paper, we study the stochastic gradient descent (SGD) method for the nonconvex nonsmooth optimization, and propose an accelerated SGD method by combining the variance reduction technique with Nesterov's extrapolation technique. Moreover, based on the local error bound condition, we establish the linear converge…
Linear-Core Surrogates combine fast optimization and statistical efficiency in classification and structured prediction.
We consider the problem of controlling a possibly unknown linear dynamical system with adversarial perturbations, adversarially chosen convex loss functions, and partially observed states, known as non-stochastic control. We introduce a controller parametrization based on the denoised observations, and prove that apply…
Study online linear regression with paid noise reduction.
Traditional Linear Genetic Programming (LGP) algorithms are based only on the selection mechanism to guide the search. Genetic operators combine or mutate random portions of the individuals, without knowing if the result will lead to a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP) methods…
New loss function handles uncertain constraints in CSLO problems.
In this paper, a novel neural network activation function, called Symmetrical Gaussian Error Linear Unit (SGELU), is proposed to obtain high performance. It is achieved by effectively integrating the property of the stochastic regularizer in the Gaussian Error Linear Unit (GELU) with the symmetrical characteristics. Co…
Single linear solve combines surface reconstruction and uncertainty quantification.
New method reduces bias in incomplete data using deliberate missingness.
New methods improve deep learning for solving linear PDEs.
The minimization of convex objectives coming from linear supervised learning problems, such as penalized generalized linear models, can be formulated as finite sums of convex functions. For such problems, a large set of stochastic first-order solvers based on the idea of variance reduction are available and combine bot…
Develops new optimization techniques for decision-making under uncertainty.
We describe novel subgradient methods for a broad class of matrix optimization problems involving nuclear norm regularization. Unlike existing approaches, our method executes very cheap iterations by combining low-rank stochastic subgradients with efficient incremental SVD updates, made possible by highly optimized and…
Study on martingale property and moment explosions in signature volatility models.
Quantum algorithm speeds up financial option pricing.
This work tackles representation learning by introducing stochastic competition-based activations.
Distributed data-parallel algorithms aim to accelerate the training of deep neural networks by parallelizing the computation of large mini-batch gradient updates across multiple nodes. Approaches that synchronize nodes using exact distributed averaging (e.g., via AllReduce) are sensitive to stragglers and communication…
Derives a pricing formula for VIX options using a new stochastic volatility model.
This work studies nonnegativity-preserving kernels for stochastic equations and their applications.
Many machine learning, statistical inference, and portfolio optimization problems require minimization of a composition of expected value functions (CEVF). Of particular interest is the finite-sum versions of such compositional optimization problems (FS-CEVF). Compositional stochastic variance reduced gradient (C-SVRG)…
We algorithmically construct multi-output Gaussian process priors which satisfy linear differential equations. Our approach attempts to parametrize all solutions of the equations using Gröbner bases. If successful, a push forward Gaussian process along the paramerization is the desired prior. We consider several exampl…
Proposes a method to estimate SDE noise from a single trajectory.
Many real-valued stochastic time-series are locally linear (Gassian), but globally non-linear. For example, the trajectory of a human hand gesture can be viewed as a linear dynamic system driven by a nonlinear dynamic system that represents muscle actions. We present a mixed-state dynamic graphical model in which a hid…
This paper deals with the exact calibration of semidiscretized stochastic local volatility (SLV) models to their underlying semidiscretized local volatility (LV) models. Under an SLV model, it is common to approximate the fair value of European-style options by semidiscretizing the backward Kolmogorov equation using fi…