arXiv research

Decentralized learning ensures stability in online queuing systems with packet rates above 1.

Improves asynchronous federated learning with queuing dynamics.

A learning-based algorithm optimizes admission control in a queuing system.

Study uses data to analyze COPD patients' impact on hospital systems.

Service system dynamics occur at the interplay between customer behaviour and a service provider's response. This kind of dynamics can effectively be modeled within the framework of queuing theory where customers' arrivals are described by point process models. However, these approaches are limited by parametric assump…

We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system. Input processes arise in many applications, including queuing systems, robotics control with disturbances, and object tracking. Since the state dynamics and rewards depend on…

The goal of this article is to describe the concepts of system dynamics and its applications to the simulation modeling of financial institutions daily activity. The hybrid method of the re-engineering of banking business processes based upon combination of system dynamics, queuing theory and tools of ordinary differen…

We present a queuing model of parking dynamics and a model-based prediction method to provide real-time probabilistic forecasts of future parking occupancy. The queuing model has a non-homogeneous arrival rate and time-varying service time distribution. All statistical assumptions of the model are verified using data f…

Asynchronous methods are widely used in deep learning, but have limited theoretical justification when applied to non-convex problems. We show that running stochastic gradient descent (SGD) in an asynchronous manner can be viewed as adding a momentum-like term to the SGD iteration. Our result does not assume convexity …

Algorithm improves reinforcement learning in MDPs with partial order policies.

We prove the existence of limiting distributions for a large class of Markov chains on a general state space in a random environment. We assume suitable versions of the standard drift and minorization conditions. In particular, the system dynamics should be contractive on the average with respect to the Lyapunov functi…

The paper analyzes fill probabilities in limit order books with varying price levels.

A hybrid model combines BPH and HE distributions for better heavy-tailed distribution approximation.

This paper proposes a system-agnostic policy for dynamic scheduling.

New RL method improves on standard discounted RL for operations research.

The paper improves regret bounds for admission control in queueing systems.

Research optimizes C++ patterns for HFT, reducing latency and improving profitability.

We analyze a method to produce pairs of non independent Poisson processes $M(t),N(t)$ from positively correlated, self-decomposable, exponential renewals. In particular the present paper provides the family of copulas pairing the renewals, along with the closed form for the joint distribution $p_{m,n}(s,t)$ of the pair…

Through the analysis of a dataset of ultra high frequency order book updates, we introduce a model which accommodates the empirical properties of the full order book together with the stylized facts of lower frequency financial data. To do so, we split the time interval of interest into periods in which a well chosen r…

New RL policy for unbounded state space with stability guarantee.

Strong stability of ergodic iterations proven without ergodic driving sequence.

New method simulates sticky boundaries in multidimensional diffusions.

In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilisti…

We study the dynamics of the limit order book of liquid stocks after experiencing large intra-day price changes. In the data we find large variations in several microscopical measures, e.g., the volatility the bid-ask spread, the bid-ask imbalance, the number of queuing limit orders, the activity (number and volume) of…

New algorithm uses control variates to improve multi-armed bandit performance.

Likelihood-free inference involves inferring parameter values given observed data and a simulator model. The simulator is computer code which takes parameters, performs stochastic calculations, and outputs simulated data. In this work, we view the simulator as a function whose inputs are (1) the parameters and (2) a ve…

Predicts next item in sequential bundles using Transformers.

We study the problem of learning policy of an infinite-horizon, discounted cost, Markov decision process (MDP) with a large number of states. We compute the actions of a policy that is nearly as good as a policy chosen by a suitable oracle from a given mixture policy class characterized by the convex hull of a set of k…

Designs for allocating resources to prioritize needy applicants while estimating treatment effects.

Finite-time queue peaks in stochastic networks have logarithmic scaling after geometric thresholds.

Study on Bitcoin transaction flows and holding times, revealing multifractal and power-law distributions.

Overview of integrable systems with symmetries, focusing on toric and semitoric systems.

New method to derive integrable systems from existing Lax systems.

Learning to control linear systems is statistically hard, especially for underactuated systems.

Discrete-time systems can be characterized by simple flat coordinates and their shifts.

The paper explores when linear system identification is hard or easy, especially for under-actuated systems.

This paper improves system identification by reducing sample complexity for high-dimensional linear dynamical systems.

In integrable hydrodynamic systems, coordinates exist where generators and symmetries are simple.

This paper studies nonholonomic constraints in Hamiltonian systems, deriving equations and theorems.

New method models unknown systems with hidden parameters using neural networks.

Study absolute equivalence for Pfaffian systems, applying to control systems.

Estimates input from output of nonlinear systems using ANN.

Solves selecting the best optimizing system problems.

This paper considers control systems defined on Lie algebroids. After deriving basic controllability tests for general control systems, we specialize our discussion to the class of mechanical control systems on Lie algebroids. This class of systems includes mechanical systems subject to holonomic and nonholonomic const…

Superintegrable systems on curved manifolds found to have Hessian structures.

Systemic risk refers to the risk that the financial system is susceptible to failures due to the characteristics of the system itself. The tremendous cost of systemic risk requires the design and implementation of tools for the efficient macroprudential regulation of financial institutions. The current paper proposes a…

Researchers solve boundary and scattering rigidity problems for magnetic systems.

The paper develops methods to derive mixed superposition rules for Lie systems and applies them to various physical systems.