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Fast and scalable likelihood maximization for Exponential Random Graph Models
Exponential Random Graph Models (ERGMs) have gained increasing popularity over the years. Rooted into statistical physics, the ERGMs framework has been successfully employed...-
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How to Make Meme Collections
How to guide to make meme collections by querying and extracting memes from four key sites: the meme database Know Your Meme, the web search engine Google Images, the meme...-
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Social Network Analysis
Social Network Analysis course @ Master SoBigData This course introduces students to the theories, concepts and measures of Social Network Analysis (SNA), that is aimed at...-
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Complex Network Analysis Lecture
These slides are for a lecture “Network analysis”, of 851-0585-38L Data Science in Techno-Socio-Economic Systems course presented in the spring semester 2021 at ETH Zurich....-
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Tutorial on Learning To Rank
Efficiency/Effectiveness Trade-offs in Learning to Rank” tutorial by Claudio Lucchese and Franco Maria Nardini at the European Conference on Machine Learning and Principles...-
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Data Journalism and Story Telling
The module aims to teach how to present the knowledge extracted from big data using multimedia story telling. It also shows some of the most recent and meaningful experiences...-
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The Role of the GDPR in Designing the European Strategy on Artificial Intelli...
Starting from an analysis of the EU Reg. n. 2016/679 on General Data Protection Regulation (GDPR), the Author deals with the opportunity to translate the current strategies on...-
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Big Data and Due Process Toward a Framework to Redress Predictive Privacy Harms
The rise of “Big Data” analytics in the private sector poses new challenges for privacy advocates. Through its reliance on existing data and predictive analysis to create... -
Big other Surveillance capitalism and the prospects of an information civiliz...
This article describes an emergent logic of accumulation in the networked sphere, ‘surveillance capitalism,’ and considers its implications for ‘information civilization.’ The... -
Compressed and Learned Data Structures Seminar
In this seminar cycle, students are guided in the direct usage of a powerful C++ library implementing many state-of-the-art compressed data structures for big data. Other than...-
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Explaining misclassification and attacks in deep learning via random forests
Artificial intelligence, and machine learning (ML) in particular, is being used for different purposes that are critical for human life. To avoid an algorithm-based...-
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Efficient detection of Byzantine attacks in federated learning using last lay...
Federated learning (FL) is an alternative to centralized machine learning (ML) that builds a model across multiple decentralized edge devices (a.k.a. workers) that own the...-
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Fair detection of poisoning attacks in federated learning
Federated learning is a decentralized machine learning technique that aggregates partial models trained by a set of clients on their own private data to obtain a global model....-
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Conformity a Path-Aware Homophily measure for Node-Attributed Networks
Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached...-
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