arXiv research

PASHA optimizes model tuning for large datasets with limited resources.

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

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

A smart method predicts and optimizes decisions online with resource constraints.

State-augmented algorithm optimizes wireless network resource management.

Optimal online learning for joint pricing and resource allocation.

ARCO-BO optimizes multi-agent design under heterogeneity, improving efficiency and performance.

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

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 optimizes resource allocation in noisy systems for better control.

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

We consider the classical problem of sequential resource allocation where a decision maker must repeatedly divide a budget between several resources, each with diminishing returns. This can be recast as a specific stochastic optimization problem where the objective is to maximize the cumulative reward, or equivalently …

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

Improved resource allocation method reduces procurement costs.

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…

Optimizes query routing to LLMs under cost and resource constraints.

OpTorch optimizes deep learning for resource-limited environments.

Adaptive Bayesian Optimization for resource-constrained experiments with switching costs.

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

Optimizes wireless network resource management with state-augmented policies.

New method optimizes resource allocation for uncertain tasks.

GP-TS optimizes TLM pre-training hyperparameters efficiently.

This paper studies the problem of optimally extracting nonrenewable natural resource in light of various financial and economic restrictions and constraints. Taking into account the fact that the market values of the main natural resources i.e. oil, natural gas, copper,...,etc, fluctuate randomly following global and s…

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

This paper studies the optimal extraction and taxation of nonrenewable natural resources. It is well known that the market values of the main strategic resources such as oil, natural gas, uranium, copper,..., etc, fluctuate randomly following global and seasonal macroeconomic parameters, these values are modeled using …

Energy savings for DNN inference on resource-constrained devices.

Optimizes resource allocation in a network with random job requests.

Framework for online resource allocation using social welfare functions.

ATA optimizes task allocation in distributed machine learning.

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…

Optimizes resource allocation for distributed parameter estimation in sensor networks.

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…

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

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…

Large multi-tenant production clusters often have to handle a variety of jobs and applications with a variety of complex resource usage characteristics. It is non-trivial and non-optimal to manually create placement rules for scheduling that would decide which applications should co-locate. In this paper, we present De…

Review of efficient neural networks for TinyML on resource-constrained devices.

A new algorithm for resource-aware multi-armed bandits minimizes regret.

Develops deep learning for optimizing 5G radio resource allocation.

Optimal design portfolios improve energy efficiency and reduce risk in uncertain reservoirs.

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

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

Paper tackles pandemic resource allocation challenges.

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

Paper proposes DP-PASGD for efficient, private IoT learning.

New estimator improves policy evaluation in resource allocation RCTs.

This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributio…

A framework for efficient multi-objective optimization using entropy search.

New algorithm solves online resource allocation problems efficiently.