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Heterodox Methods for Interpretable and Efficient Artificial Intelligence

Monday, 13. June, HG-01A32

In this workshop we will discuss (machine learning) architectures where the human involvement in the design of the model and its data ingestion process allows for both more energy efficient and more interpretable outcomes. Examples of such systems stretch from pure grammatical inference methods and probabilistic programming, where the model (family) is entirely constructed by human hands and only very specific model parameters are learned from data, to various types of interpretable neural network approaches where the specific workings of the output system is much less defined a priori. The goal is to spread knowledge about lesser known approaches to learning from data that use an increased level of human involvement, require less training data, and are tailored to achieve interpretable results in a more efficient way.

09:0009:15Welcome and introductory information
09:1510:00Invited Talk: Prof Ole-Christoffer Granmo
The Tsetlin Machine – From Arithmetic to Logic-based AI
10:0011:30Paper session 1:

Azqa Nadeem, Sicco Verwer & Shanchieh Jay Yang:
Suffix-based Finite Automata for Learning Explainable Attacker Strategies

Petter Ericson & Anna Jonsson:
Grammatical Inference: Strengths and Weaknesses

Enrique Valero-Leal, Pedro Larrañaga & Concha Bielza:
Extending MAP-independence for Bayesian network explainability

Marco Virgolin, Eric Medvet, Tanja Alderliesten & Peter A.N. Bosman:
Less is More: A Call to Focus on Simpler Modelsin Genetic Programming for Interpretable Machine Learning
11:3012:30Panel 1: Transparent models and explaining uncertainty
14:0014:45Invited Talk: Dr. Anil Yaman
On the Emergence of Collective Intelligence
14:4515:45Paper session 2:

Leila Methnani, Andreas Antoniades & Andreas Theodorou:
The AWKWARD Real-Time Adjustment of Reactive Planning

Krist Shingjergji, Deniz Iren, Felix Böttger, Corrie Urlings & Roland Klemke:
Interpretable Explainability for Face Expression Recognition

Ronald Siebes, Victor de Boer, Roberto Reda & Roderick van der Weerdt:
Learning and Reasoning over Smart Home Knowledge Graphs
15:4516:45Unconference/excursion (if the weather permits)
16:4517:45Panel 2: Transparency and efficiency in practice

Event Timeslots (3)

Monday, 13. June (Pre-C. Day 1)
Full day workshop

09:00-09:15 Welcome and introductory information 09:15-10:00 Invited Talk by Prof. Ole-Christoffer Granmo: The Tsetlin Machine – From Arithmetic to Logic-based AI 10:00-11:30 Paper session 1 11:30-12:30 Panel 1: Transparent models and explaining uncertainty

14:00-14:45 Invited Talk by Dr. Anil Yaman: On the Emergence of Collective Intelligence 14:45-15:45 Paper session 2 15:45-16:45 Unconference/excursion (if the weather permits) 16:45-17:45 Panel 2: Transparency and efficiency in practice