Satellite Workshop at the
6th International Conference on Machine Learning, Optimization & Data Science
July 23, 2020 – Certosa di Pontignano, Siena, Italy
Conference Room: Google Meet (see below)
Half-Day Workshop @ LOD 2020
Integrative Machine Learning
Machine learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns. Machine Learning solutions tend to be vertical, in the sense that they can well solve the specific task they are trained for, but the lack of emergence of a general and portable intelligence is nowadays seen as major limitation. Furthermore, machine learning is still limited when there is the need for consistent and robust decisions in complex environments, where the learning data can inevitably cover a small portion of all possible variants. The integration of machine learning and logic reasoning is an open-research problem, which could overtake these fundamental limitations, leading to the development of real intelligent agents.
This workshop covers topics in “Integrative Machine Learning”, where Machine Learning is augmented with knowledge representation and logic reasoning. In particular, we will discuss the different trade-offs on the application of constraint-based vs probabilistic solutions to represent the knowledge. Other open research problems will be discussed like: are directed or undirected models more suitable for Integrative ML? How to allow a flexible reasoning process without limiting the scalability of machine learning solutions? How to get advantage of modern tensor-based computational frameworks within integrative ML? Is an induction/deduction loop possible in machine learning like it happens in the development of human cognition?
Main topics
Integration of Machine Learning with
– Probabilistic Logic Programming
– Statistical Relational Learning
– Integer Programming and Optimization
– Information-based Principles
– Fuzzy Logic and Reasoning
– Constraint-based learning
– Graphical Models
– Knowledge Extraction and Representation
Integrative Machine Learning Program
14.40-14.50 – Welcome
14.50-15.20 – Prof. Luc de Raedt – From Probabilistic Logic Programming to Neural Symbolic Computation
15.20-15.50 – Prof. Andrea Passerini – Constructive Machine Learning
15.50-16.10 – Michelangelo Diligenti/Francesco Giannini – Relational Neural Machines
16.10-16.40 – Virtual coffee break
16.40-17.10 – Prof. Michele Lombardi – Teaching the Old Dog New Tricks: Constraint Support in Classical Supervised Learning via Declarative Optimization
17.10-17.40 – Prof. Arthur d’Avila Garcez – Neural Symbolic Computing for Trusted AI
17.40-18.10 – Round Table/Closing Remarks – Prof. Marco Gori
The Workshop will be streamed from the following google meet room:
https://meet.google.com/rga-yvtm-dkb
or at the following live stream
https://stream.meet.google.com/stream/cdba0b85-2ba6-4c34-a594-770a772feff7
Chair
Keynote Speakers
