On-device research index

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.

168,742 papers · 148 categories

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48 results for “Resource Management”

State-Augmented Learnable Algorithms for Resource Management in Wireless Networkscs.LG

State-augmented algorithm optimizes wireless network resource management.

problem Optimizing resource allocation in multi-user wireless networks.

method Proposes a state-augmented algorithm using dual variables.

result Feasible and near-optimal resource decisions achieved.

Wireless Networks Resource Management State-Augmented Graph Neural Networks Optimization

2022-07-05abs ↗pdf ↗

A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resourcesphysics.geo-ph

Deep learning enhances water resources management through data analysis.

problem Data volume and variety in water resources management.

method Systematic review of deep learning applications in hydrology and water resources.

result Deep learning improves water resources monitoring, prediction, and classification.

deep learning hydrology water resources data analysis water management

2020-06-17abs ↗pdf ↗

Robust Policy Selection and Harvest Risk Quantification for Natural Resources Management under Model Uncertaintymath.OC

Study optimizes natural resource harvesting under model uncertainty using risk measures.

problem Optimal harvesting policy selection for natural resources under model uncertainty.

method Investigated using neoclassical growth model dynamics and convex risk measures, specifically Fréchet risk measures.

result Robust harvesting strategies quantifying operational and marginal risk under model uncertainty.

resource management harvesting policy model uncertainty risk measures neoclassical growth model

2022-02-10abs ↗pdf ↗

Network Revenue Management with Demand Learning and Fair Resource-Consumption Balancingstat.ML

This paper optimizes revenue and resource balance in network revenue management.

problem Maximizing revenue while ensuring fair resource consumption across different suppliers.

method Introduces a regularized revenue objective and a primal-dual UCB algorithm for continuous prices and balancing.

result Achieves a worst-case regret of

\widetilde O(N^{5/2}\sqrt{T})

for revenue maximization and balancing.

revenue management demand learning resource balancing UCB algorithm regret minimization

2022-07-22abs ↗pdf ↗

Blind Network Revenue Management and Bandits with Knapsacks under Limited Switchescs.LG

Study on revenue management with limited switches, achieving strong performance and reduced switch counts.

problem Resource-constrained dynamic pricing with limited switching constraints.

method Developed algorithms for blind network revenue management and bandits with knapsacks, achieving optimal regret rates.

result Optimal regret rates are fully characterized by a piecewise-constant function of the switching budget and resource constraints.

revenue management dynamic pricing limited switches regret minimization online learning

2019-11-04abs ↗pdf ↗

A State-Augmented Approach for Learning Optimal Resource Management Decisions in Wireless Networkscs.LG

Optimizes wireless network resource management with state-augmented policies.

problem Optimizing network-wide utility with user performance constraints.

method State-augmented parameterization of RRM policy, using dual variables.

result Superior trade-off between mean, minimum, and 5th percentile rates.

Wireless Networks Resource Management State-Augmented Dual Variables Graph Neural Networks

2022-10-28abs ↗pdf ↗

Hindsight Learning for MDPs with Exogenous Inputscs.LG

HL algorithms improve resource allocation in cloud environments.

problem Sequential decision-making under uncertainty with exogenous variables.

method HL algorithms leverage exogenous variable samples to infer counterfactual consequences.

result HL algorithms outperform classic methods and reinforcement learning in resource allocation.

resource management sequential decision-making exogenous inputs reinforcement learning Hindsight Learning

2022-07-13abs ↗pdf ↗

Competence Assessment as an Expert System for Human Resource Management: A Mathematical Approachcs.CY

Mathematical approach assesses human resource competences accurately.

problem Accurate assessment and representation of human resource competences.

method Detailed quantification scheme and mathematical approach.

result Flexible tools for optimal job assignment and recruitment.

human resource management competence assessment mathematical approach Professional Innovative and Social competence tree

2020-01-16abs ↗pdf ↗

Stochastic perturbations and fisheries managementmath.OC

As most natural resources, fisheries are affected by random disturbances. The evolution of such resources may be modelled by a succession of deterministic process and random perturbations on biomass and/or growth rate at random times. We analyze the impact of the characteristics of the perturbations on the management o…

2019-09-04abs ↗pdf ↗

Pandemic risk management: resources contingency planning and allocationq-fin.RM

Paper tackles pandemic resource allocation challenges.

problem Shortages of medical resources during pandemics.

method Risk management approach, focusing on spatio-temporal competitions.

result New strategies for optimal stockpiling and allocation balancing resource competition.

pandemic risk resource allocation contingency planning medical supplies stockpiling

2020-12-06abs ↗pdf ↗

Learning Resilient Radio Resource Management Policies with Graph Neural Networkseess.SP

Graph neural networks optimize radio resource management policies for wireless networks.

problem Optimizing user selection and power control in wireless networks with fairness constraints.

method Formulated as a Lagrangian dual problem, RRM policies are parameterized by a GNN architecture trained on channel conditions.

result The method achieves superior tradeoff between average and 5th percentile rates, demonstrating fairness.

graph neural networks radio resource management wireless networks fairness power control

2022-03-07abs ↗pdf ↗

Simulation-based approach for Multiproject Scheduling based on composite priority rulesq-fin.PM

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.

multi-project scheduling resource allocation composite priority rules simulation portfolio management

2024-06-04abs ↗pdf ↗

Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learningeess.SP

In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems might decrease the state of coexistence. Therefore, a central coexistence management system is needed, which allocates conflict-free resourc…

2018-05-24abs ↗pdf ↗

A multivariate water quality parameter prediction model using recurrent neural networkcs.LG

Research develops a water quality prediction model using LSTM.

problem Global degradation of water resources and need for optimal water quality monitoring.

method Developed a multivariate water quality prediction model using LSTM and historical data.

result Multiple step LSTM model achieved RMSE of 0.227 mg/L.

water quality water resources recurrent neural network LSTM water management

2020-03-25abs ↗pdf ↗

Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RANcs.NI

Meta-DRL improves resource allocation in O-RAN networks.

problem Dynamic resource allocation in O-RAN networks.

method Meta Deep Reinforcement Learning (Meta-DRL) inspired by MAML.

result 19.8% improvement in network management performance.

O-RAN Meta-DRL Reinforcement Learning Resource Allocation Wireless Networks

2024-09-30abs ↗pdf ↗

Assessment of Financial Potential as a Determinant of Enterprise Developmentq-fin.GN

Paper assesses financial potential for enterprise development.

problem Determining financial potential for enterprise development.

method Stages of financial potential assessment based on literature analysis.

result Proposes a mechanism for managing enterprise financial potential.

enterprise development financial potential enterprise management financial resources strategic directions

2019-12-01abs ↗pdf ↗

EERO: Early Exit with Reject Option for Efficient Classification with limited budgetstat.ML

EERO optimizes resource usage for efficient classification.

problem Managing computational resources in complex machine learning models.

method EERO uses multiple classifiers with a reject option to adaptively shorten processing paths.

result EERO effectively manages budget allocation and enhances accuracy in complex scenarios.

machine learning resource management early exit reject option Bayesian risk

2024-02-06abs ↗pdf ↗

ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learningcs.LG

ATA optimizes task allocation in distributed machine learning.

problem Greedy task allocation leads to inefficiencies in distributed machine learning.

method Adaptive Task Allocation (ATA) adapts to unknown computation time distributions.

result ATA identifies optimal task allocation without prior knowledge of computation times.

distributed machine learning task allocation resource management asynchronous methods greedy approach

2025-02-02abs ↗pdf ↗

A Deep Learning Strategy for Vehicular Floating Content Managementcs.NI

Floating Content (FC) is a communication paradigm for the local dissemination of contextualized information through D2D connectivity, in a way which minimizes the use of resources while achieving some specified performance target. Existing approaches to FC dimensioning are based on unrealistic system assumptions that m…

2018-10-24abs ↗pdf ↗

Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learningcs.LG

A deep RL framework optimizes resource allocation in wireless networks.

problem Optimizing resource allocation and interference in wireless networks.

method Multi-agent deep reinforcement learning for distributed decision-making.

result Our approach outperforms decentralized and centralized baselines in terms of user rates.

wireless networks resource management deep reinforcement learning interference mitigation distributed decision-making

2020-02-14abs ↗pdf ↗

Cell-Free Data Power Control Via Scalable Multi-Objective Bayesian Optimisationeess.SY

Optimizes data power control in cell-free networks for better spectral efficiency.

problem Maximizing overall spectral efficiency in cell-free networks with multi-objective optimisation.

method Applied scalable multi-objective Bayesian optimisation to solve convergence-time limitations.

result Improved radio resource management in cell-free networks.

cell-free networks Bayesian optimisation multi-objective optimisation spectral efficiency

2022-12-20abs ↗pdf ↗

When Multiple Agents Learn to Schedule: A Distributed Radio Resource Management Frameworkcs.LG

Interference among concurrent transmissions in a wireless network is a key factor limiting the system performance. One way to alleviate this problem is to manage the radio resources in order to maximize either the average or the worst-case performance. However, joint consideration of both metrics is often neglected as …

2019-06-20abs ↗pdf ↗

Machine Learning assisted Handover and Resource Management for Cellular Connected Droneseess.SP

Solves challenges of drone communication in cellular networks.

problem Interference from drones to base stations in cellular networks.

method Derived analytical models, formulated optimization problem, transformed into machine learning problem, solved using deep reinforcement learning.

result Optimal handover and resource management policies for drones in cellular networks.

cellular networks drones handover resource management reinforcement learning

2020-01-22abs ↗pdf ↗

Efficient crowdsourcing of crowd-generated microtaskscs.HC

Cost forecasting helps crowdsourcers manage growing task sets efficiently.

problem Crowdsourcers face resource overwhelm with growing task sets.

method Cost forecasting to decide between new tasks or existing ones based on cost efficiency.

result Cost forecasting improves accuracy and efficiency in crowdsourcing.

crowdsourcing microtasks resource management algorithmic approaches cost forecasting

2019-12-10abs ↗pdf ↗

Toward Runtime-Throttleable Neural Networkscs.LG

As deep neural network (NN) methods have matured, there has been increasing interest in deploying NN solutions to "edge computing" platforms such as mobile phones or embedded controllers. These platforms are often resource-constrained, especially in energy storage and power, but state-of-the-art NN architectures are de…

2019-05-30abs ↗pdf ↗

A simple and effective predictive resource scaling heuristic for large-scale cloud applicationscs.DC

Simple policy outperforms complex ones in cloud auto-scaling.

problem Predicting resource scaling for large-scale cloud applications with limited deployment throughput.

method Probabilistic workload forecast for auto-scaling decisions based on risk aversion.

result The proposed policy outperforms sophisticated and simple benchmark policies in real-world and synthetic data.

cloud computing auto-scaling resource management workload forecasting risk aversion

2020-08-03abs ↗pdf ↗

Langevin algorithms for Markovian Neural Networks and Deep Stochastic controlq-fin.CP

Langevin algorithms enhance training of deep neural networks for stochastic control problems.

problem Training acceleration for deep neural networks in stochastic control problems.

method Application of Langevin algorithms to minimize the loss of deep neural networks in stochastic control problems.

result Langevin algorithms improve training on various stochastic control problems.

Langevin algorithms deep neural networks stochastic control gradient descent resource management

2022-12-22abs ↗pdf ↗

CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19cs.LG

CPAS uses machine learning to plan hospital resources for COVID-19.

problem Forecasting hospital resource demands during the COVID-19 pandemic.

method Combining machine learning algorithms with diverse data sources.

result CPAS successfully managed hospital resource planning in the UK.

machine learning hospital capacity planning COVID-19 pandemic resource management

2020-07-27abs ↗pdf ↗

Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approachcs.LG

A DRL approach optimizes resource allocation in BFL to reduce latency and energy consumption.

problem Energy and CPU constraints of mobile devices and increased training latency due to blockchain mining.

method Deep Reinforcement Learning (DRL) to derive optimal decisions for MLMO.

result Optimal resource allocation and block generation rate to minimize system latency, energy consumption, and incentive cost.

Blockchain Federated Learning Deep Reinforcement Learning Resource Management Mobile Devices

2020-04-08abs ↗pdf ↗

Sequence-to-sequence models for workload interferencecs.LG

Sequence-to-sequence models predict resource usage for co-scheduled jobs in data centers.

problem Challenges in co-scheduling jobs due to resource interference and inefficiencies.

method Sequence-to-sequence models based on recurrent neural networks for workload interference prediction.

result Models accurately forecast resource usage trends from job profiles, improving scheduling decisions.

sequence-to-sequence workload interference resource prediction data centers machine learning

2020-06-25abs ↗pdf ↗

DeepFloat: Resource-Efficient Dynamic Management of Vehicular Floating Contentcs.NI

Opportunistic communications are expected to playa crucial role in enabling context-aware vehicular services. A widely investigated opportunistic communication paradigm for storing a piece of content probabilistically in a geographica larea is Floating Content (FC). A key issue in the practical deployment of FC is how …

2019-06-11abs ↗pdf ↗

Resource Constrained Deep Reinforcement Learningcs.LG

In urban environments, supply resources have to be constantly matched to the "right" locations (where customer demand is present) so as to improve quality of life. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in EMS (Emergency Management Sy…

2018-12-03abs ↗pdf ↗

Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Modelphysics.ao-ph

These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the countrys environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and det…

2019-04-17abs ↗pdf ↗

HydroNets: Leveraging River Structure for Hydrologic Modelingcs.LG

HydroNets use river structure to improve hydrologic predictions.

problem Scalable and accurate hydrologic models are needed for climate change impacts.

method HydroNets are deep neural networks that incorporate river network structure.

result HydroNets improve predictions with fewer data, especially at longer horizons.

hydrologic modeling river networks deep learning climate change water resource management

2020-07-01abs ↗pdf ↗

Generative AI Enhanced Financial Risk Management Information Retrievalq-fin.RM

Paper introduces RiskEmbed, a finetuned model for financial risk management.

problem Improving retrieval accuracy in financial question-answering systems.

method Curated dataset and finetuned BERT model for financial domain.

result RiskEmbed significantly outperforms general-purpose and financial embedding models.

financial risk management financial question-answering BERT embedding retrieval accuracy financial institutions

2025-04-04abs ↗pdf ↗

Robust Batch-Level Query Routing for Large Language Models under Cost and Capacity Constraintscs.LG

Optimizes query routing to LLMs under cost and resource constraints.

problem Non-uniform or adversarial batching in per-query routing methods leads to cost inefficiency.

method Batch-level, resource-aware routing framework that jointly optimizes model assignment for each batch.

result Robust routing framework improves accuracy by 1-14% over non-robust methods.

query routing large language models cost constraints resource management batch-level optimization

2026-03-25abs ↗pdf ↗

Bandits with Replenishable Knapsacks: the Best of both Worldscs.LG

New framework handles online decisions with replenishable resources, improving both adversarial and stochastic performance.

problem Online decision-making with resource constraints that can be replenished.

method Best-of-both-worlds primal-dual template for online learning problems with replenishment.

result First positive results for adversarial inputs and an instance-independent regret bound for stochastic inputs.

online learning resource management replenishment competitive ratio regret minimization

2023-06-14abs ↗pdf ↗

Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problemcs.LG

Deep RL tackles fleet management and dispatching for ride-sharing platforms.

problem Optimizing dispatching and repositioning of drivers in ride-sharing platforms.

method Deep reinforcement learning approach treating drivers as a central system agent.

result Centralized decision-making improves overall fleet efficiency.

reinforcement learning fleet management ride-sharing multi-agent systems dispatching

2019-11-25abs ↗pdf ↗

Reinforcement Learning-based Application Autoscaling in the Cloud: A Surveycs.DC

RL enhances cloud autoscaling by learning optimal resource allocation.

problem Optimizing resource allocation in dynamic, uncertain cloud environments.

method Reinforcement Learning for dynamic, transparent, and adaptable resource management policies.

result RL enables transparent, dynamic, and adaptable resource management policies.

Reinforcement Learning Autoscaling Cloud Computing Resource Management Dynamic Environment

2020-01-27abs ↗pdf ↗

POCAII: Parameter Optimization with Conscious Allocation using Iterative Intelligencecs.LG

POCAII optimizes hyperparameters with a new approach, showing superior performance.

problem Hyperparameter optimization with limited resources.

method Explicitly separates search and evaluation phases, focusing on exploration and exploitation.

result POCAII outperforms state-of-the-art HPO algorithms in low-budget scenarios.

hyperparameter optimization parameter tuning resource management iterative intelligence explore-exploit

2025-05-16abs ↗pdf ↗

Network Security under Heterogeneous Cyber-Risk Profiles and Contagioncs.CR

This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.

problem Optimizing cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.

method Combining strategic behavior of players with contagion dynamics, a method is extended to determine optimal resource allocation based on simple network metrics weighted by risk profiles.

result The asymmetry between attacker and defender valuations drives optimal attack and defense strategies, shaping system resilience.

cybersecurity resource allocation network security contagion dynamics heterogeneous risk profiles

2026-01-23abs ↗pdf ↗

Demand Balancing in Primal-Dual Optimization for Blind Network Revenue Managementstat.ML

Improved algorithm reduces regret in NRM with unknown demand.

problem Optimizing prices for products with limited resources under unknown demand.

method Primal-dual optimization with demand balancing.

result Improved regret bound of

O(N^{3.25}\sqrt{T})

network revenue management primal-dual optimization demand balancing regret minimization nonparametric demand

2024-04-06abs ↗pdf ↗

Graph database while computationally efficient filters out quickly the ESG integrated equities in investment managementq-fin.CP

Graph database outperforms in filtering ESG stocks efficiently.

problem Efficiently filtering ESG stocks from large lists of equities.

method Compared SQL, No-SQL, and graph databases; used Python for database interactions; collected data from stock price and financial news.

result Graph database is more efficient for ESG stock filtering.

Graph database ESG integration investment management SQL No-SQL

2024-01-15abs ↗pdf ↗

Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicingcs.LG

Network slicing promises to provision diversified services with distinct requirements in one infrastructure. Deep reinforcement learning (e.g., deep $\mathcal{Q}$ -learning, DQL) is assumed to be an appropriate algorithm to solve the demand-aware inter-slice resource management issue in network slicing by regarding the …

2019-06-10abs ↗pdf ↗

Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemicq-bio.PE

Model predicts COVID-19 cases with high accuracy using mobility data.

problem Accurate forecasting of COVID-19 cases for resource management.

method Heterogeneous infection rate model with human mobility, linearization, weighted least squares.

result Extremely accurate predictions of confirmed cases at country and state levels.

COVID-19 epidemic modeling human mobility forecasting resource management

2020-04-23abs ↗pdf ↗

Beyond Tides and Time: Machine Learning Triumph in Water Qualitystat.ML

LightGBM outperforms other models in predicting pH values in Georgia, USA.

problem Accurate water quality prediction for effective resource management and pollution mitigation.

method Five distinct predictive models (linear regression, Random Forest, XGBoost, LightGBM, MLP neural network) were assessed for pH value forecasting in Georgia, USA.

result LightGBM achieved the highest average precision in predicting pH values.

water quality pH prediction Georgia LightGBM machine learning

2023-09-29abs ↗pdf ↗

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RL: Mind-aware Multi-agent Management Reinforcement Learningcs.AI

Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly controlling the agents to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we consider scenarios where there are self-interested agents (i.e., worker agent…

2018-09-29abs ↗pdf ↗

Surrogate Optimization of Deep Neural Networks for Groundwater Predictionsstat.ML

Sustainable management of groundwater resources under changing climatic conditions require an application of reliable and accurate predictions of groundwater levels. Mechanistic multi-scale, multi-physics simulation models are often too hard to use for this purpose, especially for groundwater managers who do not have a…

2019-08-28abs ↗pdf ↗