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Hierarchical Mean-Field Type Control of Price Dynamics for Electricity in Smart Grid

Journal of Systems Science & Complexity 2022年第1期35卷 1-17页

作者： FRIHI Zahrate El Oula CHOUTRI Salah Eddine BARREIRO-GOMEZ Julian tembine hamidou Laboratory of Probability and Statistics(La PS) Department of MathematicsBadji-Mokhtar UniversityB.P.12Annaba 23000Algeria. NYUAD Research Institute New York University Abu DhabiUAE Learning&Game Theory Laboratory New York University Abu DhabiSaadiyat Campus PO Box 129188UAE

This paper solves a mean-field type hierarchical optimal control problem in electricity *** authors consider n-1 prosumers and one *** ith prosumer,for 1 详细信息

This paper solves a mean-field type hierarchical optimal control problem in electricity *** authors consider n-1 prosumers and one *** ith prosumer,for 1

关键词： Electricity price dynamics hierarchical control mean-field-type control smart grids

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Bregman Learning for Generative Adversarial Networks

Bregman Learning for Generative Adversarial Networks

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作者： Jian Gao hamidou tembine Learning ＆ Game Theory Laboratory Engineering DivisionNew York University

In this paper we develop a game theoretic learning framework for deep generative ***,the problem of minimizing the dissimilarity between the generator distribution and real data is introduced based on ***,the optimization problem is transformed into a zero-sum game with two adversarial players,and the existence of Nash equilibrium is established in the quasi-concave-convex case under suitable ***,a general Bregman-based learning algorithm is proposed to find the Nash *** algorithm is proved to have a doubly logarithmic convergence time with respect to the precision of the minimax value in potential convex ***,our methodology is implemented in three application scenarios and compared with several existing optimization *** qualitative and quantitative evaluation show that the generative model trained by our algorithm has the state-of-art performance.

关键词： Bregman learning GAN Convex Optimization Deep Neural Network Game Theory

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Generative Model of Bid Sequences in Lowest Unique Bid Auctions

Generative Model of Bid Sequences in Lowest Unique Bid Aucti...

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第30届中国控制与决策会议

作者： Yida Xu hamidou tembine Learning & Game Theory Laboratory New York University Abu Dhabi Tandon School of Engineering New York University

Lowest unique bid auction(LUBA) sites are gaining popularity on the Internet in recent years. In this paper, we study LUBA with resubmission in discrete bid spaces. A long-standing goal in the field of Internet auction is to develop agents that can perceive and understand the strategy information behind the mechanism and can guide us to behave in a fast, frugal and smart way. We marry ideas from recurrent neural network and data to learn a generative model for generating winning bid sequences. A sequence of winning bids in Internet auctions can be viewed as a sequence of events and modeled by generative models. We learn a model that is able to capture the long dependencies in a winning bid sequence. The generated data obtained from our model and the ground truth dataset share similar distributions.

关键词： LUBA auction game theory generative model recurrent neural network

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Distributed Representations of Bids in Lowest Unique Bid Auctions

Distributed Representations of Bids in Lowest Unique Bid Auc...

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第30届中国控制与决策会议

作者： Yida Xu hamidou tembine Learning & Game Theory Laboratory New York University Abu Dhabi Tandon School of Engineering New York University

In this paper, we study multi-item lowest unique bid auctions（LUBA） with resubmission under budget constraints. A representation of bids that can perceive the rich strategic meaning in the world of multi-item LUBA with resubmission is learned by an efficient method–Skip-gram model. We created and maintained a dataset which contains the relevant information of LUBA, including the winning bid combination, budget constraints, and the number of participated bidders. An Android-based application is developed and contributes the construction of the dataset. The quantitative analysis displays that the representation cluster can reflect similarities in terms of numerical information,cultural and aesthetic preference, and other bidder’s behaviors. The learned representation can serve as a guide for bidders who seek to maximize their payoff and feed into a sequence generation model, such as recurrent neural network,to produce the bids combination.

关键词： LUBA auction game theory embedding space

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