arXiv research

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

Algorithm tackles clustered contextual bandits with resource constraints.

A dueling bandit problem with resource constraints is solved using EXP3.

Paper tackles non-monotonic resource utilization in sequential decision-making.

The paper benchmarks data stream classifiers for human activity recognition on connected devices.

We consider the linear contextual bandit problem with resource consumption, in addition to reward generation. In each round, the outcome of pulling an arm is a reward as well as a vector of resource consumptions. The expected values of these outcomes depend linearly on the context of that arm. The budget/capacity const…

We introduce the logistic model of consumption growth, which captures a negative feedback loop preventing an unlimited growth of consumption due to finite biophysical resources of our planet. This simple dynamic model allows for derivation of the expression describing the declining long-term tail of a social discount c…

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

Probability distributions of money, income, and energy consumption per capita are studied for ensembles of economic agents. The principle of entropy maximization for partitioning of a limited resource gives exponential distributions for the investigated variables. A non-equilibrium difference of money temperatures betw…

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

OL4EL optimizes edge learning on resource-constrained servers.

We study a general problem of allocating limited resources to heterogeneous customers over time under model uncertainty. Each type of customer can be serviced using different actions, each of which stochastically consumes some combination of resources, and returns different rewards for the resources consumed. We consid…

Federated learning optimizes task and resource allocation in balloon networks.

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…

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

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

TensorOpt finds optimal parallelization strategies for DNN training.

Real-time semantic segmentation for autonomous vehicles on FPGA reduces latency and power consumption.

Enhanced tabular benchmarks for energy-efficient neural architecture search.

This research optimizes energy consumption forecasting in Puno using parallel computing and ARIMA models.

Paper tackles online DR-submodular maximization with stochastic constraints.

Energy savings for DNN inference on resource-constrained devices.

New algorithm for contextual bandits with linear constraints using regression.

PSQRNN model forecasts electricity consumption in China by integrating neural networks and quantile regression.

We consider a contextual version of multi-armed bandit problem with global knapsack constraints. In each round, the outcome of pulling an arm is a scalar reward and a resource consumption vector, both dependent on the context, and the global knapsack constraints require the total consumption for each resource to be bel…

This paper optimizes caching and model multiplexing for large model inference.

A model is presented in this work for simulating endogenously the evolution of the marginal costs of production of energy carriers from non-renewable resources, their consumption, depletion pathways and timescales. Such marginal costs can be used to simulate the long term average price formation of energy commodities. …

PePR scores assess DL model performance per resource unit, promoting smaller, more efficient models.

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

TabNAS improves neural architecture search for tabular datasets by rejecting suboptimal architectures.

We study the global probability distribution of energy consumption per capita around the world using data from the U.S. Energy Information Administration (EIA) for 1980-2010. We find that the Lorenz curves have moved up during this time period, and the Gini coefficient G has decreased from 0.66 in 1980 to 0.55 in 2010,…

Pseudo rehearsal uses non-photo-realistic images to save resources without sacrificing performance.

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches. These approaches aim for a carefully chosen trade-off between performance and resource consumption in terms of computat…

A new text classification method using separable convolution reduces memory consumption.

This paper tackles energy-efficient machine learning on low-power devices.

Study non-stationary bandits with resource constraints.

There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data. Despite its advantages in data privacy-preserving, Federated Learning (FL) still has …

EERO optimizes resource usage for efficient classification.

New algorithm reduces online decision-making regret with efficient LP re-solving and parallel first-order method.

Algorithm maximizes rewards with a budget and giving up option.

CICLAD efficiently mines frequent closed itemsets from data streams with minimal memory usage.

Neural Architecture Search aims at automatically finding neural architectures that are competitive with architectures designed by human experts. While recent approaches have achieved state-of-the-art predictive performance for image recognition, they are problematic under resource constraints for two reasons: (1)the ne…

Develops deep learning for optimizing 5G radio resource allocation.

SEFR is a fast, energy-efficient classifier for ultra-low power devices.

HATCH learns optimal recommendations with resource constraints.

In this paper, the problem of energy efficient transmission and computation resource allocation for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then,…

In the coming years, the satellite broadband market will experience significant increases in the service demand, especially for the mobility sector, where demand is burstier. Many of the next generation of satellites will be equipped with numerous degrees of freedom in power and bandwidth allocation capabilities, makin…

Optimal algorithm for maximizing rewards in contextual bandits with resource constraints.