arXiv research

The paper develops algorithms to minimize queue length regret in a communication system.

Algorithm stabilizes queues in asymmetric systems with unknown service rates.

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

Study improves queue length estimation from connected vehicles by filtering parameters.

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

The paper tackles ride-hailing fleet repositioning with a calibrated demand approach.

This paper attempts to find out numerically the distribution of the queue-length ratio in the context of a model of preferential attachment. Here we consider two restaurants only and a large number of customers (agents) who come to these restaurants. Each day the same number of agents sequentially arrives and decides w…

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…

New RL algorithm reduces regret in birth-death queueing problems.

We introduce Delay Pruning, a simple yet powerful technique to regularize dynamic Boltzmann machines (DyBM). The recently introduced DyBM provides a particularly structured Boltzmann machine, as a generative model of a multi-dimensional time-series. This Boltzmann machine can have infinitely many layers of units but al…

An online learning framework optimizes pricing and capacity in service systems.

New clustering method uses Wasserstein distance to analyze simulation outputs.

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

Optimal hidden-target learning for online inventory optimization on general convex sets.

Study on queues with Hawkes arrivals, proving steady-state behavior and developing an efficient algorithm.

The paper analyzes transaction fees on blockchains using a priority queue model.

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

Improved queue-reactive model considers order sizes for better market simulation.

New algorithm reduces regret bounds for Bayesian optimization with unknown hyperparameters.

MERLIN tackles multi-objective task scheduling with hierarchical DRL, outperforming existing methods.

Enhances queue-reactive model for realistic limit order book simulation.

Modern Internet services, such as those at Google, Yahoo!, and Amazon, handle billions of requests per day on clusters of thousands of computers. Because these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. Such services are model…

We study adaptive regret bounds in terms of the variation of the losses (the so-called path-length bounds) for both multi-armed bandit and more generally linear bandit. We first show that the seemingly suboptimal path-length bound of (Wei and Luo, 2018) is in fact not improvable for adaptive adversary. Despite this neg…

Algorithm minimizes regret and converges to equilibria in Markov games.

The paper models blockchain queues and trading dynamics, finding conditions for transaction priority and price impact.

New online conformal prediction methods minimize strongly adaptive regret and achieve near-optimal coverage.

Improved dynamic regret analysis for strongly convex and smooth functions.

Order positions are key variables in algorithmic trading. This paper studies the limiting behavior of order positions and related queues in a limit order book. In addition to the fluid and diffusion limits for the processes, fluctuations of order positions and related queues around their fluid limits are analyzed. As a…

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

Paper analyzes regret bounds for unconstrained online optimization.

Motivated by empirical data, we develop a statistical description of the queue dynamics for large tick assets based on a two-dimensional Fokker-Planck (diffusion) equation, that explicitly includes state dependence, i.e. the fact that the drift and diffusion depends on the volume present on both sides of the spread. "J…

Paper uses queue theory to model financial signals with relativistic delay.

In this work we introduce two variants of multivariate Hawkes models with an explicit dependency on various queue sizes aimed at modeling the stochastic time evolution of a limit order book. The models we propose thus integrate the influence of both the current book state and the past order flow. The first variant cons…

The paper optimizes LLM inference systems through queueing theory.

Unified model for market dynamics, linking price and order flow.

In a financial market, for agents with long investment horizons or at times of severe market stress, it is often changes in the asset price that act as the trigger for transactions or shifts in investment position. This suggests the use of price thresholds to simulate agent behavior over much longer timescales than are…

Recursive least-squares algorithms often use forgetting factors as a heuristic to adapt to non-stationary data streams. The first contribution of this paper rigorously characterizes the effect of forgetting factors for a class of online Newton algorithms. For exp-concave and strongly convex objectives, the algorithms a…

This paper considers a cross-layer adaptive modulation system that is modeled as a Markov decision process (MDP). We study how to utilize the monotonicity of the optimal transmission policy to relieve the computational complexity of dynamic programming (DP). In this system, a scheduler controls the bit rate of the m-qu…

New Q-learning algorithms reduce regret in inventory control problems.

Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift. Class imbalance deals with data streams that have very skewed distributions while concept drift deals with changes in the class imbalance s…

We investigate whether the bid/ask queue imbalance in a limit order book (LOB) provides significant predictive power for the direction of the next mid-price movement. We consider this question both in the context of a simple binary classifier, which seeks to predict the direction of the next mid-price movement, and a p…

We study the Thompson sampling algorithm in an adversarial setting, specifically, for adversarial bit prediction. We characterize the bit sequences with the smallest and largest expected regret. Among sequences of length $T$ with $k < \frac{T}{2}$ zeros, the sequences of largest regret consist of alternating zeros and …

We develop a novel and generic algorithm for the adversarial multi-armed bandit problem (or more generally the combinatorial semi-bandit problem). When instantiated differently, our algorithm achieves various new data-dependent regret bounds improving previous work. Examples include: 1) a regret bound depending on the …

Model for cross-border markets with limited transmission capacities.

Dynamic regret minimization is shown equivalent to static regret minimization for linear losses.

New algorithm reduces regret in stochastic shortest path problems.

New algorithms reduce regret in online MDPs by adapting to data and variance.

The paper analyzes the sliding regret of stochastic bandit algorithms.