262 research outputs found
Data-driven solutions to enhance planning, operation and design tools in Industry 4.0 context
This thesis proposes three different data-driven solutions to be combined to state-of-the-art solvers and tools in order to primarily enhance their computational performances. The problem of efficiently designing the open sea floating platforms on which wind turbines can be mount on will be tackled, as well as the tuning of a data-driven engine's monitoring tool for maritime transportation. Finally, the activities of SAT and ASP solvers will be thoroughly studied and a deep learning architecture will be proposed to enhance the heuristics-based solving approach adopted by such software. The covered domains are different and the same is true for their respective targets. Nonetheless, the proposed Artificial Intelligence and Machine Learning algorithms are shared as well as the overall picture: promote Industrial AI and meet the constraints imposed by Industry 4.0 vision. The lesser presence of human-in-the-loop, a data-driven approach to discover causalities otherwise ignored, a special attention to the environmental impact of industries' emissions, a real and efficient exploitation of the Big Data available today are just a subset of the latter. Hence, from a broader perspective, the experiments carried out within this thesis are driven towards the aforementioned targets and the resulting outcomes are satisfactory enough to potentially convince the research community and industrialists that they are not just "visions" but they can be actually put into practice. However, it is still an introduction to the topic and the developed models are at what can be defined a "pilot" stage. Nonetheless, the results are promising and they pave the way towards further improvements and the consolidation of the dictates of Industry 4.0
Learning Large-Scale Bayesian Networks with the sparsebn Package
Learning graphical models from data is an important problem with wide
applications, ranging from genomics to the social sciences. Nowadays datasets
often have upwards of thousands---sometimes tens or hundreds of thousands---of
variables and far fewer samples. To meet this challenge, we have developed a
new R package called sparsebn for learning the structure of large, sparse
graphical models with a focus on Bayesian networks. While there are many
existing software packages for this task, this package focuses on the unique
setting of learning large networks from high-dimensional data, possibly with
interventions. As such, the methods provided place a premium on scalability and
consistency in a high-dimensional setting. Furthermore, in the presence of
interventions, the methods implemented here achieve the goal of learning a
causal network from data. Additionally, the sparsebn package is fully
compatible with existing software packages for network analysis.Comment: To appear in the Journal of Statistical Software, 39 pages, 7 figure
LiFT: A Scalable Framework for Measuring Fairness in ML Applications
Many internet applications are powered by machine learned models, which are
usually trained on labeled datasets obtained through either implicit / explicit
user feedback signals or human judgments. Since societal biases may be present
in the generation of such datasets, it is possible for the trained models to be
biased, thereby resulting in potential discrimination and harms for
disadvantaged groups. Motivated by the need for understanding and addressing
algorithmic bias in web-scale ML systems and the limitations of existing
fairness toolkits, we present the LinkedIn Fairness Toolkit (LiFT), a framework
for scalable computation of fairness metrics as part of large ML systems. We
highlight the key requirements in deployed settings, and present the design of
our fairness measurement system. We discuss the challenges encountered in
incorporating fairness tools in practice and the lessons learned during
deployment at LinkedIn. Finally, we provide open problems based on practical
experience.Comment: Accepted for publication in CIKM 202
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