The Experts below are selected from a list of 113631 Experts worldwide ranked by ideXlab platform
Clarisse Dhaenens - One of the best experts on this subject based on the ideXlab platform.
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LION - Feature Selection Using Tabu Search with Learning Memory: Learning Tabu Search
Lecture Notes in Computer Science, 2016Co-Authors: Lucien Mousin, Laetitia Jourdan, Marie-eléonore Marmion, Clarisse DhaenensAbstract:Feature selection in classification can be modeled as a combinatorial optimization problem. One of the main particularities of this problem is the large amount of time that may be needed to evaluate the quality of a subset of features. In this paper, we propose to solve this problem with a tabu search algorithm integrating a Learning Mechanism. To do so, we adapt to the feature selection problem, a Learning tabu search algorithm originally designed for a railway network problem in which the evaluation of a solution is time-consuming. Experiments are conducted and show the benefit of using a Learning Mechanism to solve hard instances of the literature.
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Feature Selection using Tabu Search with Learning Memory: Learning Tabu Search
2016Co-Authors: Lucien Mousin, Laetitia Jourdan, Marie-eléonore Marmion, Clarisse DhaenensAbstract:Feature selection in classification can be modeled as a com-binatorial optimization problem. One of the main particularities of this problem is the large amount of time that may be needed to evaluate the quality of a subset of features. In this paper, we propose to solve this problem with a tabu search algorithm integrating a Learning Mechanism. To do so, we adapt to the feature selection problem, a Learning tabu search algorithm originally designed for a railway network problem in which the evaluation of a solution is time-consuming. Experiments are conducted and show the benefit of using a Learning Mechanism to solve hard instances of the literature.
Nicola Gatti - One of the best experts on this subject based on the ideXlab platform.
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a truthful Learning Mechanism for contextual multi slot sponsored search auctions with externalities
Electronic Commerce, 2012Co-Authors: Nicola Gatti, Alessandro Lazaric, Francesco TrovoAbstract:Sponsored search auctions constitute one of the most successful applications of microeconomic Mechanisms. In Mechanism design, auctions are usually designed to incentivize advertisers to bid their truthful valuations and, at the same time, to assure both the advertisers and the auctioneer a non--negative utility. Nonetheless, in sponsored search auctions, the click-through-rates (CTRs) of the advertisers are often unknown to the auctioneer and thus standard incentive compatible Mechanisms cannot be directly applied and must be paired with an effective Learning algorithm for the estimation of the CTRs. This introduces the critical problem of designing a Learning Mechanism able to estimate the CTRs as the same time as implementing a truthful Mechanism with a revenue loss as small as possible compared to an optimal Mechanism designed with the true CTRs. Previous works showed that in single-slot auctions the problem can be solved using a suitable exploration-exploitation Mechanism able to achieve a per-step regret of order O(T-1/3) (where T is the number of times the auction is repeated). In this paper we extend these results to the general case of contextual multi-slot auctions with position- and ad-dependent externalities. In particular, we prove novel upper-bounds on the revenue loss w.r.t. to a VCG auction and we report numerical simulations investigating their accuracy in predicting the dependency of the regret on the number of rounds T, the number of slots K, and the number of advertisements n.
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A Truthful Learning Mechanism for Contextual Multi--Slot Sponsored Search Auctions with Externalities
2012Co-Authors: Nicola Gatti, Alessandro Lazaric, Francesco Trov'{o}Abstract:Sponsored search auctions constitute one of the most successful applications of \emph{microeconomic Mechanisms}. In Mechanism design, auctions are usually designed to incentivize advertisers to bid their truthful valuations and, at the same time, to assure both the advertisers and the auctioneer a non--negative utility. Nonetheless, in sponsored search auctions, the click--through--rates (CTRs) of the advertisers are often unknown to the auctioneer and thus standard incentive compatible Mechanisms cannot be directly applied and must be paired with an effective Learning algorithm for the estimation of the CTRs. This introduces the critical problem of designing a Learning Mechanism able to estimate the CTRs as the same time as implementing a truthful Mechanism with a revenue loss as small as possible compared to an optimal Mechanism designed with the true CTRs. Previous works showed that in single--slot auctions the problem can be solved using a suitable exploration--exploitation Mechanism able to achieve a per--step regret of order $O(T^{-1/3})$ (where $T$ is the number of times the auction is repeated). In this paper we extend these results to the general case of contextual multi--slot auctions with position-- and ad--dependent externalities. In particular, we prove novel upper--bounds on the revenue loss w.r.t. to a VCG auction and we report numerical simulations investigating their accuracy in predicting the dependency of the regret on the number of rounds $T$, the number of slots $K$, and the number of advertisements $n$.
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A Truthful Learning Mechanism for Contextual Multi-Slot Sponsored Search Auctions with Externalities
2012Co-Authors: Alessandro Lazaric, Nicola Gatti, Trov'{o} FrancescoAbstract:Sponsored search auctions constitute one of the most successful applications of microeconomic Mechanisms. In particular, pay-per-click auctions have been designed to incentivize advertisers to bid their truthful valuations and, at the same time, to assure both the advertisers and the auctioneer a non--negative utility. Nonetheless, when the click-through-rates (CTRs) of the advertisers are unknown to the auction, these Mechanisms must be paired with an effective Learning algorithm for the estimation of the CTRs. This introduces the critical problem of designing a Learning Mechanism able to estimate the CTRs as the same time as implementing a truthful Mechanism with a revenue loss as small as possible. Previous works showed that in single-slot auctions the problem can be solved using a suitable exploration-exploitation Mechanism able to achieve a per-step regret of order $O(T^{-1/3})$ (where $T$ is the number of times the auction is repeated). In this paper we extend these results to the general case of contextual multi-slot auctions with position- and ad-dependent externalities. In particular, we prove novel upper-bounds on the revenue loss w.r.t. to a generalized VCG auction and we report numerical simulations investigating their accuracy in predicting the dependency of the regret on the number of rounds $T$, the number of slots $K$, and the number of advertisements $n$.
Judy Oliver - One of the best experts on this subject based on the ideXlab platform.
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Continuous improvement: role of organisational Learning Mechanisms
International Journal of Quality & Reliability Management, 2009Co-Authors: Judy OliverAbstract:Purpose – The purpose of this study is to explore the use of the performance measurement system as an organizational Learning Mechanism to support continuous improvement.Design/methodology/approach – The paper reports the results of a survey of Australian organizations certified to quality standard ISO 9000.Findings – For those respondents who consider their organization's quality program to be successful, the findings indicate that such organizations have embedded quality into the culture of the organization, and have developed performance measurement systems as an organizational Learning Mechanism to support the continuous improvement initiatives.Practical implications – The paper highlights the need for management to ensure that the organization's management control systems are structured to support continuous improvement initiatives.Originality/value – The paper explores the links between continuous improvement and organizational Learning.
Lucien Mousin - One of the best experts on this subject based on the ideXlab platform.
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LION - Feature Selection Using Tabu Search with Learning Memory: Learning Tabu Search
Lecture Notes in Computer Science, 2016Co-Authors: Lucien Mousin, Laetitia Jourdan, Marie-eléonore Marmion, Clarisse DhaenensAbstract:Feature selection in classification can be modeled as a combinatorial optimization problem. One of the main particularities of this problem is the large amount of time that may be needed to evaluate the quality of a subset of features. In this paper, we propose to solve this problem with a tabu search algorithm integrating a Learning Mechanism. To do so, we adapt to the feature selection problem, a Learning tabu search algorithm originally designed for a railway network problem in which the evaluation of a solution is time-consuming. Experiments are conducted and show the benefit of using a Learning Mechanism to solve hard instances of the literature.
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Feature Selection using Tabu Search with Learning Memory: Learning Tabu Search
2016Co-Authors: Lucien Mousin, Laetitia Jourdan, Marie-eléonore Marmion, Clarisse DhaenensAbstract:Feature selection in classification can be modeled as a com-binatorial optimization problem. One of the main particularities of this problem is the large amount of time that may be needed to evaluate the quality of a subset of features. In this paper, we propose to solve this problem with a tabu search algorithm integrating a Learning Mechanism. To do so, we adapt to the feature selection problem, a Learning tabu search algorithm originally designed for a railway network problem in which the evaluation of a solution is time-consuming. Experiments are conducted and show the benefit of using a Learning Mechanism to solve hard instances of the literature.
Alessandro Lazaric - One of the best experts on this subject based on the ideXlab platform.
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a truthful Learning Mechanism for contextual multi slot sponsored search auctions with externalities
Electronic Commerce, 2012Co-Authors: Nicola Gatti, Alessandro Lazaric, Francesco TrovoAbstract:Sponsored search auctions constitute one of the most successful applications of microeconomic Mechanisms. In Mechanism design, auctions are usually designed to incentivize advertisers to bid their truthful valuations and, at the same time, to assure both the advertisers and the auctioneer a non--negative utility. Nonetheless, in sponsored search auctions, the click-through-rates (CTRs) of the advertisers are often unknown to the auctioneer and thus standard incentive compatible Mechanisms cannot be directly applied and must be paired with an effective Learning algorithm for the estimation of the CTRs. This introduces the critical problem of designing a Learning Mechanism able to estimate the CTRs as the same time as implementing a truthful Mechanism with a revenue loss as small as possible compared to an optimal Mechanism designed with the true CTRs. Previous works showed that in single-slot auctions the problem can be solved using a suitable exploration-exploitation Mechanism able to achieve a per-step regret of order O(T-1/3) (where T is the number of times the auction is repeated). In this paper we extend these results to the general case of contextual multi-slot auctions with position- and ad-dependent externalities. In particular, we prove novel upper-bounds on the revenue loss w.r.t. to a VCG auction and we report numerical simulations investigating their accuracy in predicting the dependency of the regret on the number of rounds T, the number of slots K, and the number of advertisements n.
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A Truthful Learning Mechanism for Contextual Multi--Slot Sponsored Search Auctions with Externalities
2012Co-Authors: Nicola Gatti, Alessandro Lazaric, Francesco Trov'{o}Abstract:Sponsored search auctions constitute one of the most successful applications of \emph{microeconomic Mechanisms}. In Mechanism design, auctions are usually designed to incentivize advertisers to bid their truthful valuations and, at the same time, to assure both the advertisers and the auctioneer a non--negative utility. Nonetheless, in sponsored search auctions, the click--through--rates (CTRs) of the advertisers are often unknown to the auctioneer and thus standard incentive compatible Mechanisms cannot be directly applied and must be paired with an effective Learning algorithm for the estimation of the CTRs. This introduces the critical problem of designing a Learning Mechanism able to estimate the CTRs as the same time as implementing a truthful Mechanism with a revenue loss as small as possible compared to an optimal Mechanism designed with the true CTRs. Previous works showed that in single--slot auctions the problem can be solved using a suitable exploration--exploitation Mechanism able to achieve a per--step regret of order $O(T^{-1/3})$ (where $T$ is the number of times the auction is repeated). In this paper we extend these results to the general case of contextual multi--slot auctions with position-- and ad--dependent externalities. In particular, we prove novel upper--bounds on the revenue loss w.r.t. to a VCG auction and we report numerical simulations investigating their accuracy in predicting the dependency of the regret on the number of rounds $T$, the number of slots $K$, and the number of advertisements $n$.
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A Truthful Learning Mechanism for Contextual Multi-Slot Sponsored Search Auctions with Externalities
2012Co-Authors: Alessandro Lazaric, Nicola Gatti, Trov'{o} FrancescoAbstract:Sponsored search auctions constitute one of the most successful applications of microeconomic Mechanisms. In particular, pay-per-click auctions have been designed to incentivize advertisers to bid their truthful valuations and, at the same time, to assure both the advertisers and the auctioneer a non--negative utility. Nonetheless, when the click-through-rates (CTRs) of the advertisers are unknown to the auction, these Mechanisms must be paired with an effective Learning algorithm for the estimation of the CTRs. This introduces the critical problem of designing a Learning Mechanism able to estimate the CTRs as the same time as implementing a truthful Mechanism with a revenue loss as small as possible. Previous works showed that in single-slot auctions the problem can be solved using a suitable exploration-exploitation Mechanism able to achieve a per-step regret of order $O(T^{-1/3})$ (where $T$ is the number of times the auction is repeated). In this paper we extend these results to the general case of contextual multi-slot auctions with position- and ad-dependent externalities. In particular, we prove novel upper-bounds on the revenue loss w.r.t. to a generalized VCG auction and we report numerical simulations investigating their accuracy in predicting the dependency of the regret on the number of rounds $T$, the number of slots $K$, and the number of advertisements $n$.
