Speaker: Daniele Castellana

Title: Machine Learning in Structured Domains

Abstract:

Machine Learning (ML) for structured data aims to build ML models which can handle structured data (e.g. sequence, trees, and graphs). In this setting, common ML approaches for flat data (i.e. vectors) cannot be applied directly. In this seminar, we show the challenges of learning from structured data and discuss how to overcome them. We introduce a tensor framework for learning from trees highlighting how tensors naturally arise in this context and the role of tensor decompositions to reduce the computational complexity of such approach. Interestingly, this framework can be applied to both probabilistic and neural models for trees. Lastly, we talk about learning from graphs. In particular, we introduce a probabilistic model for graph based on a Bayesian Non-Parametric technique showing its effectiveness in inferring suitable hyper-parameters.

Speaker: Andrea Marino

Title: Algorithms for the Analysis of (Temporal) Graphs

Abstract:

In a society in which we are almost always “connected”, the capability of acquiring different information has become a source of huge amounts of data to be elaborated and analysed. Graphs are data structures that allow modeling the relationships in these data, effectively representing millions of nodes and billions of connections between nodes as edges. For example, a social network can be seen as a graph whose nodes are the users and whose edges correspond to the friendships between them. In such a context, discovering the most important or influential users, which communities they belong to, and which is the structure of their society can be translated into discovering suitable patterns on graphs. Sometimes in some scenarios additional constraints must be considered: for instance, in temporal graphs, connections are available only at prescribed times, in the same way as connections of a public transportation system are available according to a time schedule. In temporal graphs, walks and paths make sense only if the legs of the trip are time consistent, i.e. the departure of a leg is after the arrival of the previous one, and structural patterns must take into account this constraint. Our work is devoted to understand whether such patterns can be computed efficiently, i.e. in time polynomial wrt the size of the graph, hopefully linearly if the considered graphs are big. During the talk we will see a high level overview of our contributions in the field.

Speaker: Francesco Tiezzi

Title: A Tale on Domain-Specific System Engineering: the Case of Multi-Robot Systems

Abstract:

The first part of the seminar introduces challenges and methodologies concerning the (possibly formal) engineering of domain-specific distributed systems. The second part focuses on a specific case: the development of software for robotics applications involving multiple heterogeneous robots. Programming such distributed software requires coordinating multiple cooperating tasks to avoid unsatisfactory emerging behaviors. It is then presented a programming language devised to face this challenge. The language supports an approach for programming multi-robot systems at a high abstraction level, allowing developers to focus on the system behavior, achieving readable, reusable, and maintainable code.