arXiv research

Optimal resource allocation in censored semi-bandits with unknown thresholds.

Study resource allocation strategies in sequential decisions with unknown rewards.

Paper tackles pandemic resource allocation challenges.

Study optimizes resource allocation in noisy systems for better control.

New loss function reduces outage probability in ML-assisted resource allocation.

Resource allocation improved using machine learning from terminal positions.

We study classification problems where features are corrupted by noise and where the magnitude of the noise in each feature is influenced by the resources allocated to its acquisition. This is the case, for example, when multiple sensors share a common resource (power, bandwidth, attention, etc.). We develop a method f…

Unified formula for arbitrary liquidity operations in weighted AMMs

This paper proposes a decentralized reinforcement learning method for multi-agent resource allocation.

Generative profiling improves real-time task timing for varied resource contexts.

Driven by the tremendous technological advancement of personal devices and the prevalence of wireless mobile network accesses, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a better viewers quality of experience (QoE) is the key to maximize the audiences number and increase streaming pro…

Optimal online learning for joint pricing and resource allocation.

The paper tackles resource allocation for arms with unknown and random rewards, achieving optimal regret bounds.

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

The paper evaluates index-based allocation policies using data from randomized control trials.

ATA optimizes task allocation in distributed machine learning.

We study a sequential resource allocation problem between a fixed number of arms. On each iteration the algorithm distributes a resource among the arms in order to maximize the expected success rate. Allocating more of the resource to a given arm increases the probability that it succeeds, yet with a cut-off. We follow…

RL optimizes resource allocation in MG by balancing experience and exploration.

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…

Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances to facilitate stochastic resource allocation tasks. By recognizing the central role of Lagrange multipliers, the unde…

Improved resource allocation method reduces procurement costs.

PASHA optimizes model tuning for large datasets with limited resources.

Study develops a smart contract framework for efficient and fair resource allocation.

New estimator improves policy evaluation in resource allocation RCTs.

Some agent-based models for growth and allocation of resources are described. The first class considered consists of conservative models, where the number of agents and the size of resources are constant during time evolution. The second class is made up of multiplicative noise models and some of their extensions to co…

Develops deep learning for optimizing 5G radio resource allocation.

Framework for online resource allocation using social welfare functions.

Paper proposes OPF policy for fair resource allocation with sublinear regret.

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

The virtualization of compute and network resources enables an unseen flexibility for deploying network services. A wide spectrum of emerging technologies allows an ever-growing range of orchestration possibilities in cloud-based environments. But in this context it remains challenging to rhyme dynamic cloud configurat…

New algorithm tackles resource allocation in multi-armed bandits to balance speed and throughput.

Algorithm allocates perishable resources online to minimize envy and inefficiency.

This work tackles resource allocation in asynchronous and stochastic systems.

This paper considers the design of optimal resource allocation policies in wireless communication systems which are generically modeled as a functional optimization problem with stochastic constraints. These optimization problems have the structure of a learning problem in which the statistical loss appears as a constr…

Study efficient resource allocation for detecting extreme values.

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

Uber optimizes marketplace levers using machine learning to improve resource allocation efficiency.

Adaptive RL optimizes testing resource allocation for dynamic software environments.

DSA efficiently allocates sparsity across layers for budgeted pruning.

This paper tackles resource allocation in the Lightning Network using DRL.

New algorithm optimizes online network resource allocation with long-term constraints.

New method optimizes resource allocation for uncertain tasks.

Study allocates resources to strategic agents while balancing cost and incentives.

We show that, in a resource allocation problem, the ex ante aggregate utility of players with cumulative-prospect-theoretic preferences can be increased over deterministic allocations by implementing lotteries. We formulate an optimization problem, called the system problem, to find the optimal lottery allocation. The …

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…

In this paper, we study censored Semi-Bandits, a novel variant of the semi-bandits problem. The learner is assumed to have a fixed amount of resources, which it allocates to the arms at each time step. The loss observed from an arm is random and depends on the amount of resources allocated to it. More specifically, the…

The paper tackles online resource allocation with uncertain coefficients and chance constraints.

The paper proposes an online algorithm for network resource allocation with reduced costs.