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.
Develops a stochastic approach to financial market delays.
New algorithm tackles delayed feedback in Lipschitz bandits with sublinear regret.
Deep neural networks solve stochastic control problems with delay.
The paper solves optimal control problems for stochastic delay equations.
Delay-SDE-net models time series with memory and uncertainty, outperforming other models.
New algorithm tackles stochastic bandits with varying arm-dependent delays.
New method solves stochastic control problems with delays using deep learning.
We provide tight finite-time convergence bounds for gradient descent and stochastic gradient descent on quadratic functions, when the gradients are delayed and reflect iterates from rounds ago. First, we show that without stochastic noise, delays strongly affect the attainable optimization error: In fact, the error…
Study proves convergence of interest rate model approximations.
DASA speeds up SA with delayed agents, achieving N-fold speedup.
Stochastic linear bandits are a natural and well-studied model for structured exploration/exploitation problems and are widely used in applications such as online marketing and recommendation. One of the main challenges faced by practitioners hoping to apply existing algorithms is that usually the feedback is randomly …
Proposes a deep learning method for solving complex financial games with delays.
Understanding the convergence performance of asynchronous stochastic gradient descent method (Async-SGD) has received increasing attention in recent years due to their foundational role in machine learning. To date, however, most of the existing works are restricted to either bounded gradient delays or convex settings.…
We study distributed stochastic convex optimization under the delayed gradient model where the server nodes perform parameter updates, while the worker nodes compute stochastic gradients. We discuss, analyze, and experiment with a setup motivated by the behavior of real-world distributed computation networks, where the…
This article is a sequel to [A.H.M.P]. In [A.H.M.P], we develop an explicit formula for pricing European options when the underlying stock price follows a non-linear stochastic delay equation with fixed delays in the drift and diffusion terms. In this article, we look at models of the stock price described by stochasti…
Optimal trading strategy between CEXs and DEXs with priority fees and stochastic delays.
Enhances SGLD for log-concave posteriors with asynchronous computation.
We study a variant of the stochastic -armed bandit problem, which we call "bandits with delayed, aggregated anonymous feedback". In this problem, when the player pulls an arm, a reward is generated, however it is not immediately observed. Instead, at the end of each round the player observes only the sum of a number…
The paper models financial asset prices with jumps and evaluates European option prices using numerical methods.
We consider that the price of a firm follows a non linear stochastic delay differential equation. We also assume that any claim value whose value depends on firm value and time follows a non linear stochastic delay differential equation. Using self-financed strategy and replication we are able to derive a Random Partia…
In this article we propose a model for stochastic delay differential equation with jumps (SDDEJ) in a differentiable manifold endowed with a connection . In our model, the continuous part is driven by vector fields with a fixed delay and the jumps are assumed to come from a distinct source of (càdlàg) noise…
Improved algorithm for bandits with delayed feedback, combining adversarial and stochastic performance.
We analyze the convergence of gradient-based optimization algorithms that base their updates on delayed stochastic gradient information. The main application of our results is to the development of gradient-based distributed optimization algorithms where a master node performs parameter updates while worker nodes compu…
New algorithm reduces distributed optimization time with stochastic delays.
We propose a model of inter-bank lending and borrowing which takes into account clearing debt obligations. The evolution of log-monetary reserves of banks is described by coupled diffusions driven by controls with delay in their drifts. Banks are minimizing their finite-horizon objective functions which take into a…
This paper presents a stochastic logic time delay reservoir design. The reservoir is analyzed using a number of metrics, such as kernel quality, generalization rank, performance on simple benchmarks, and is also compared to a deterministic design. A novel re-seeding method is introduced to reduce the adverse effects of…
Stochastic delay differential equations (SDDE's) have been used for financial modeling. In this article, we study a SDDE obtained by the equation of a CIR process, with an additional fixed delay term in drift; in particular, we prove that there exists a unique strong solution (positive and integrable) which we call fix…
New algorithm reduces regret in delayed feedback generalised linear bandits.
Study optimal portfolios for traders with asymmetric information and delay.
New method speeds up training of large kernel models.
We analyze (stochastic) gradient descent (SGD) with delayed updates on smooth quasi-convex and non-convex functions and derive concise, non-asymptotic, convergence rates. We show that the rate of convergence in all cases consists of two terms: (i) a stochastic term which is not affected by the delay, and (ii) a higher …
Paper develops Euler scheme for fractional delay diff. eqs with additive noise.
New algorithm tackles non-stationary delayed feedback in recommender systems.
This study analyzes satellite communication latency using a stochastic geometry model.
Adapts two algorithms for online learning with delayed rewards.
This paper investigates a hybrid stochastic differential reinsurance and investment game between one reinsurer and two insurers, including a stochastic Stackelberg differential subgame and a non-zero-sum stochastic differential subgame. The reinsurer, as the leader of the Stackelberg game, can price reinsurance premium…
Paper tackles dueling bandits with delayed feedback, revealing preference bias.
Develops a new bivariate process for energy markets with improved simulation methods.
Novel algorithm for decentralized optimization in time-varying networks with delays.
We propose an optimal portfolio problem in the incomplete market where the underlying assets depend on economic factors with delayed effects, such models can describe the short term forecasting and the interaction with time lag among different financial markets. The delay phenomenon can be recognized as the integral ty…
Online learning with delayed feedback has received increasing attention recently due to its several applications in distributed, web-based learning problems. In this paper we provide a systematic study of the topic, and analyze the effect of delay on the regret of online learning algorithms. Somewhat surprisingly, it t…
Algorithm improves RL by discovering delayed causal relations.
New algorithms ensure fair selection in combinatorial semi-bandit with unrestricted delays.
Study on interest rate model with jumps, proving strong convergence in simulations.
In this paper we study the problem of convergence and generalization error bound of stochastic momentum for deep learning from the perspective of regularization. To do so, we first interpret momentum as solving an -regularized minimization problem to learn the offsets between arbitrary two successive model para…
In this paper we investigate novel applications of a new class of equations which we call time-delayed backward stochastic differential equations. Time-delayed BSDEs may arise in finance when we want to find an investment strategy and an investment portfolio which should replicate a liability or meet a target depending…
Adaptive distributed SGD reduces delay in slow workers.
Study cooperative bandit learning with imperfect communication, achieving near-optimal performance.