arXiv research

LaMBO optimizes modular systems with switching costs, achieving better results than existing methods.

New method assesses energy storage value beyond cost reduction.

Study analyzes cost-benefit of CBM for unmanned systems.

New algorithm reduces costs in wind energy systems by minimizing decision changes.

Germany's tax admin costs likely exceed 20% of total revenue, requiring system improvement.

Efficient algorithm for unknown linear systems with convex costs.

New algorithm controls linear systems with bandit feedback, achieving optimal regret.

In this work, a new approach for Sun tracking systems is presented. Due to the current system limitations regarding costs and operational problems, a new approach based on low cost, computer vision open hardware and deep learning has been developed. The preliminary tests carried out successfully in Plataforma solar de …

New system assigns vehicles to routes for cost and energy efficiency.

The grid integration of intermittent Renewable Energy Sources (RES) causes costs for grid operators due to forecast uncertainty and the resulting production schedule mismatches. These so-called profile service costs are marginal cost components and can be understood as an insurance fee against RES production schedule u…

The study sets limits on how well systems can be controlled adaptively.

Optimal control in changing systems without strong convexity assumptions.

The paper reveals the hidden costs of digitizing commodity money and proposes a new stable-coin system.

Paper tackles online control of linear systems with unbounded noise.

Study examines barriers to grid-connected battery systems in Spain, finding high cycle cost remains main obstacle.

Meta-learning control algorithm with finite-time guarantees for unknown systems.

This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function. We present a method, based on convex optimization, that accomplishes this task robustly: i.e., we minimize the worst-case cost, accounting for system uncertainty given the observed …

In markets with transaction costs, consistent price systems play the same role as martingale measures in frictionless markets. We prove that if a continuous price process has conditional full support, then it admits consistent price systems for arbitrarily small transaction costs. This result applies to a large class o…

Under proportional transaction costs, a price process is said to have a consistent price system, if there is a semimartingale with an equivalent martingale measure that evolves within the bid-ask spread. We show that a continuous, multi-asset price process has a consistent price system, under arbitrarily small proporti…

In a market with transaction costs, the price of a derivative can be expressed in terms of (preconsistent) price systems (after Kusuoka (1995)). In this paper, we consider a market with binomial model for stock price and discuss how to generate the price systems. From this, the price formula of a derivative can be refo…

New method disentangles perceptual uncertainty and behavioral costs in partially observable systems.

Proposes a new method for predicting uncertain net electricity demand.

CoE modularizes LLMs for scalable, cost-effective AI systems.

FAB-COST improves cold-start recommendation accuracy with less data.

Systemic risk refers to the risk that the financial system is susceptible to failures due to the characteristics of the system itself. The tremendous cost of systemic risk requires the design and implementation of tools for the efficient macroprudential regulation of financial institutions. The current paper proposes a…

New method handles robust and adaptive control of linear systems with non-convex costs.

New attacks reduce bad queries in black-box classifiers, improving effectiveness.

Interpretable meta-learning for physical systems reduces computational costs and improves interpretability.

Incentive-aware recommender system for online platforms.

Researchers develop neural optimal transport with Lagrangian costs for efficient computation.

Efficiently identifies promising hyperparameters for online learning models.

Optimizes state monitoring in Markovian systems with cost constraints.

Study how transaction costs impact stock returns and holdings in equilibrium.

The usability and practicality of any machine learning (ML) applications are largely influenced by two critical but hard-to-attain factors: low latency and low cost. Unfortunately, achieving low latency and low cost is very challenging when ML depends on real-world data that are highly distributed and rapidly growing (…

New method quantifies resilience of electric distribution systems from historical data.

We consider a finite horizon optimal stopping problem related to trade-off strategies between expected profit and cost cash-flows of an investment under uncertainty. The optimal problem is first formulated in terms of a system of Snell envelopes for the profit and cost yields which act as obstacles to each other. We th…

New trading policies preserve robust gains in presence of transaction costs.

New pricing framework allocates costs of operating reserves and transmission.

SVD-based methods reduce computational cost for stochastic systems.

We study the problem of adaptive control of a high dimensional linear quadratic (LQ) system. Previous work established the asymptotic convergence to an optimal controller for various adaptive control schemes. More recently, for the average cost LQ problem, a regret bound of ${O}(\sqrt{T})$ was shown, apart form logarit…

A new learning framework reduces PV-Battery system costs by 3.6%.

Generative models speed up complex system simulations.

Formulates LGFO to measure fair ML systems using legal signals.

The excessively increased volume of data in modern data management systems demands an improved system performance, frequently provided by data distribution, system scalability and performance optimization techniques. Optimized horizontal data partitioning has a significant influence of distributed data management syste…

This paper proposes an agent-based model that combines both spot and balancing electricity markets. From this model, we develop a multi-agent simulation to study the integration of the consumers' flexibility into the system. Our study identifies the conditions that real-time prices may lead to higher electricity costs,…

In this note, we study the utility maximization problem on the terminal wealth under proportional transaction costs and bounded random endowment. In particular, we restrict ourselves to the numéraire-based model and work with utility functions only supporting R+. Under the assumption of existence of consistent price sy…

Optimizes costs in uncertain Markov systems using risk filters.

Non-bilinear observations make optimal control harder, showing non-convex costs and non-affine optimal controllers.