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arXiv research

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.

168,695 papers · 148 categories

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2468 · Jul 202019922001200920172026
48 results for priority queues

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

problem Understanding and optimizing transaction fees on blockchain networks.
method An M/G^K/1 priority queue model is used to analyze transaction fees and user behavior.
result New insights into the dynamics of transaction fees and their impact on user behavior are provided.

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

problem Resource allocation under uncertainty with prioritized queues.
method Priority-queue randomization for treatment assignment and estimation of treatment effects.
result Identification of causal effects under different arrival and treatment assignment scenarios.

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

problem Understanding and predicting price impacts in blockchain trading environments.
method Developed a probabilistic model for blockchain queues with adversarial scheduling, derived expressions for transaction priority and price impact.
result Conditions for transaction priority and statistical models for price impact in blockchain trading environments.

Blockchain markets with paid-priority trading can lead to biased prices and reduced liquidity.

problem Discrete clearing and paid-priority in blockchain markets lead to biased prices and reduced liquidity.
method Developed a model to evaluate the viability of blockchain markets under discrete clearing and paid-priority.
result Paid-priority ordering induces endogenous selection, leading to biased prices and reduced liquidity.

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

problem Minimizing the difference between actual and optimal queue lengths over time slots.
method Introduces queue length regret and applies algorithms from stochastic multi-armed bandit problem to analyze system performance.
result Order optimal O(1)O(1) queue length regret can be achieved with queue-length based policies.

Fast classification for sparse models, even with correlated features.

problem Sparse classification with many correlated features.
method Linear and quadratic surrogate cuts, priority queue, and analytical solution for exponential loss.
result 2 to 5 times faster than previous approaches, interpretable models with comparable accuracy.

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

problem Analyzing the steady-state behavior of queues with Hawkes arrivals.
method Novel coupling techniques and exponential convergence results for workload and busy period processes.
result Exponential convergence of queueing processes to their stationary distribution.

Algorithm stabilizes queues in asymmetric systems with unknown service rates.

problem Stabilizing queues in multi-class multi-server systems with unknown service rates.
method Proposes UCB and Thompson Sampling algorithms to stabilize queues while learning service rates.
result Achieves system stability with an average queue length bound of \(O(\min\{N,K\}/ε)\) for large time horizon \(T\).

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

problem Optimizing multiple conflicting constraints in multi-objective task scheduling with varying queue sizes.
method Hierarchical deep reinforcement learning approach to manage large queues efficiently.
result MERLIN outperforms existing methods by a large margin (>22%) on multiple queue sizes.

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

problem Realistic simulation of limit order books for market research and strategy development.
method Extends Queue-Reactive model with neural network for complex dependencies and varying market conditions.
result Captures key market properties like square-root law of market impact and order size patterns.

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…

2015-05-18abs ↗pdf ↗

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

problem Large errors in estimated queue lengths at low market penetration rates.
method Used Kalman and Particle filters as multilevel real-time estimators.
result Filters reduce estimation errors and improve accuracy within 15 minutes.

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

problem Repositioning idle supply before future demand is observed in ride-hailing.
method A predict-then-optimize approach using calibrated demand regimes, a similarity gate, and spatial queue-regret decomposition.
result The spatial gate reduces mean wait time to 82.3s compared to 85.3s for a hand-tuned similarity gate and 85.8s for a distributional-only baseline.

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…

2018-05-11abs ↗pdf ↗

Enhances multi-project scheduling with multiple priority rules.

problem Resource allocation in multi-project scheduling with limited time and resources.
method Simulation-based approach using composite priority rules.
result Increased probability of finding schedules with shortest duration.

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…

2013-04-25abs ↗pdf ↗

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…

2019-01-25abs ↗pdf ↗

The paper optimizes LLM inference systems through queueing theory.

problem Efficient LLM inference for AI agents under various routing topologies.
method Developed a fluid-limit framework for multi-class batched processing networks under K-FCFS scheduling.
result Proved that work-conserving scheduling algorithms maximize throughput for LLM inference.

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

problem Modeling market dynamics and order flow in a unified framework.
method Markovian market model driven by a hidden Brownian efficient price, signal-driven and queue-reactive models.
result Stability of mid-price around efficient price at macroscopic scale, behavior as diffusion.

Optimal trading strategy between CEXs and DEXs with priority fees and stochastic delays.

problem Managing latency risk in trading between centralized and decentralized exchanges.
method Developed a mixed control framework combining absolutely continuous controls with impulse interventions, allowing for stochastic execution delays and multiple pending orders.
result Optimal priority fee selection significantly outperforms non-strategic fee selection.

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…

2018-09-27abs ↗pdf ↗

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…

2008-08-23abs ↗pdf ↗

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

problem Improving regret bounds for admission control in queueing systems.
method Proposes an algorithm inspired by UCRL2 and uses problem structure to bound regret.
result Proves an upper bound on the expected total regret of O(SlogT+mTlogT)O(S\log T + \sqrt{mT \log T}).

Neural networks learn clean data patterns first, then noisy data, leading to improved performance initially but deteriorating later.

problem Improvement in prediction error on clean data during early training of neural networks with noisy labels.
method Theoretical analysis and experiments to explore the dynamics of gradient descent and the impact of clean and noisy data.
result Neural networks prioritize learning clean data patterns first, leading to improved performance initially but deteriorating later due to diminishing gradient dominance of clean samples over noisy ones.

Proposes a new method for feature selection using Bayesian ID with intervention.

problem Feature selection in data with varying importance.
method Probabilistic model for interpolative decomposition with Bayesian inference and Gibbs sampling.
result The proposed Bayesian ID algorithm with intervention selects features with higher priority and comparable reconstructive errors.

Paper improves volatility estimation using a Queue-Reactive model.

problem Volatility estimation from high-frequency data is biased by microstructure noise.
method Uses Queue-Reactive model of limit order book to improve volatility estimation.
result Unified and alternation estimators lead to optimal mean squared error for integrated volatility.

We examine the dynamics of the bid and ask queues of a limit order book and their relationship with the intensity of trade arrivals. In particular, we study the probability of price movements and trade arrivals as a function of the quote imbalance at the top of the limit order book. We propose a stochastic model in an …

2013-12-02abs ↗pdf ↗

The study examines how backrun auctions can protect traders from price manipulation.

problem Price manipulation by arbitrageurs in batched trading venues.
method Developed a laminated queueing model to study price manipulation and introduced a price manipulation coefficient.
result Bound the price manipulation coefficient and found it approximated by a 'zeta value' with measurable parameters.

IMM uses imitation learning and predictive representation learning to improve market making strategies.

problem Challenges in training RL agents for multi-price level market making strategies.
method IMM combines RL and imitation learning, introducing effective state and action representations and a representation learning unit.
result IMM outperforms existing RL-based market making strategies in financial criteria.

Gittins index optimizes decision-making under uncertainty, even in complex scenarios.

problem Optimal decision-making under uncertainty.
method Gittins index optimizes allocation of resources among uncertain options.
result Gittins index can be effectively applied to practical problems, including Bayesian optimization and queue latency minimization.

Silent abandonment reduces contact center efficiency by 5%-15%.

problem Measuring customer abandonment and patience in text-based contact centers is challenging due to uncertainty.
method Developed methodologies to identify silent-abandonment customers and estimate customer patience using text analysis and queueing models.
result Silent abandonment accounts for 30%-67% of customer abandonments and reduces system efficiency by 5%-15%.

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

problem Optimizing admission decisions in a queuing system with unknown parameters.
method Proposes a learning-based dispatching algorithm to minimize regret compared to optimal policies.
result Achieves optimal regret bounds for different scenarios of unknown parameters.