arXiv research

HardCoRe-NAS finds fitting neural networks adhering to hard resource constraints.

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

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

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

Algorithm tackles clustered contextual bandits with resource constraints.

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

Optimizes query routing to LLMs under cost and resource constraints.

Study non-stationary bandits with resource constraints.

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…

New framework guides resource usage to achieve sublinear regret in adversarial settings.

State-augmented algorithm optimizes wireless network resource management.

HATCH learns optimal recommendations with resource constraints.

We present MorphNet, an approach to automate the design of neural network structures. MorphNet iteratively shrinks and expands a network, shrinking via a resource-weighted sparsifying regularizer on activations and expanding via a uniform multiplicative factor on all layers. In contrast to previous approaches, our meth…

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 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…

Paper tackles online DR-submodular maximization with stochastic constraints.

Algorithm ensures privacy while strictly adhering to constraints.

Study capacity constraints in continual learning with a simple model.

Voltage control plays an important role in the operation of electricity distribution networks, especially with high penetration of distributed energy resources. These resources introduce significant and fast varying uncertainties. In this paper, we focus on reactive power compensation to control voltage in the presence…

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

A new pricing controller handles resource constraints to infer target prices effectively.

LinearAPT optimizes decision-making under resource constraints for a linear threshold problem.

OL4EL optimizes edge learning on resource-constrained servers.

Study on collaboration vs. independent data collection in sensor networks.

Caratheodory's axiom limits arbitrage in resource-limited systems.

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

New algorithm tackles dynamic assortment optimization with knapsack constraints.

Proposes rounding method for precise treatment effect estimation under budget constraints.

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

Optimizes wireless network resource management with state-augmented policies.

Many Internet-of-Things (IoT) applications demand fast and accurate understanding of a few key events in their surrounding environment. Deep Convolutional Neural Networks (CNNs) have emerged as an effective approach to understand speech, images, and similar high dimensional data types. Algorithmic performance of modern…

New algorithm for contextual bandits with linear constraints using regression.

Study scaling of optimal solutions for reliability constraints in resource provisioning.

Optimizes resource allocation in a network with random job requests.

This work tackles maintenance planning with deep reinforcement learning under uncertainty.

Paper uses RL to solve constrained combinatorial optimization problems.

Proposes a new framework for resource-limited recommendation.

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

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

Method generates resource-optimized ML models for different platforms.

New approach tackles resource constraints in bandit problems with weakly adaptive algorithms.

Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints - i.e. decision-makers are bounded in their rationality. Here we experimentally tested the predic…

This paper introduces the first asymptotically optimal strategy for a multi armed bandit (MAB) model under side constraints. The side constraints model situations in which bandit activations are limited by the availability of certain resources that are replenished at a constant rate. The main result involves the deriva…

A new framework for coreset selection in machine learning models.

We propose to prune a random forest (RF) for resource-constrained prediction. We first construct a RF and then prune it to optimize expected feature cost & accuracy. We pose pruning RFs as a novel 0-1 integer program with linear constraints that encourages feature re-use. We establish total unimodularity of the constra…

Classification tasks are common across many fields and applications where the decision maker's action is limited by resource constraints. In direct marketing only a subset of customers is contacted; scarce human resources limit the number of interviews to the most promising job candidates; limited donated organs are pr…

Training a model to perform a task typically requires a large amount of data from the domains in which the task will be applied. However, it is often the case that data are abundant in some domains but scarce in others. Domain adaptation deals with the challenge of adapting a model trained from a data-rich source domai…

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…