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Jordi Nin

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2 papers

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JMLR Journal 2023 Journal Article

A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers

Nahuel Statuto
Irene Unceta
Jordi Nin
Oriol Pujol

Differential replication through copying refers to the process of replicating the decision behavior of a machine learning model using another model that possesses enhanced features and attributes. This process is relevant when external constraints limit the performance of an industrial predictive system. Under such circumstances, copying enables the retention of original prediction capabilities while adapting to new demands. Previous research has focused on the single-pass implementation for copying. This paper introduces a novel sequential approach that significantly reduces the amount of computational resources needed to train or maintain a copy, leading to reduced maintenance costs for companies using machine learning models in production. The effectiveness of the sequential approach is demonstrated through experiments with synthetic and real-world datasets, showing significant reductions in time and resources, while maintaining or improving accuracy. [abs] [ pdf ][ bib ] &copy JMLR 2023. ( edit, beta )

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AAAI Conference 2015 Conference Paper

CrowdWON: A Modelling Language for Crowd Processes based on Workflow Nets

David Sanchez-Charles
Victor Muntes-Mulero
Marc Sole
Jordi Nin

Although crowdsourcing has been proven efficient as a mechanism to solve independent tasks for on-line production, it is still unclear how to define and manage workflows in complex tasks that require the participation and coordination of different workers. Despite the existence of different frameworks to define workflows, we still lack a commonly accepted solution that is able to describe the most common workflows in current and future platforms. In this paper, we propose Crowd- WON, a new graphical framework to describe and monitor crowd processes, the proposed language is able to represent the workflow of most well-known existing applications, extend previous modelling frameworks, and assist in the future generation of crowdsourcing platforms. Beyond previous proposals, CrowdWON allows for the formal definition of adaptative workflows, that depend on the skills of the crowd workers and/or process deadlines. CrowdWON also allows expressing constraints on workers based on previous individual contributions. Finally, we show how our proposal can be used to describe well known crowdsourcing workflows.

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