I gave a chat within the workshop on how the synthesis of logic and device Understanding, Particularly spots for example statistical relational learning, can empower interpretability.
I might be offering a tutorial on logic and Mastering that has a center on infinite domains at this 12 months's SUM. Backlink to celebration below.
I gave a chat entitled "Perspectives on Explainable AI," at an interdisciplinary workshop specializing in making have faith in in AI.
I attended the SML workshop while in the Black Forest, and talked about the connections amongst explainable AI and statistical relational Studying.
An post at the scheduling and inference workshop at AAAI-eighteen compares two distinct approaches for probabilistic scheduling by the use of probabilistic programming.
I gave a talk on our recent NeurIPS paper in Glasgow though also covering other approaches for the intersection of logic, Finding out and tractability. Due to Oana with the invitation.
We have a fresh paper approved on Discovering optimum linear programming goals. We acquire an “implicit“ speculation construction method that yields wonderful theoretical bounds. Congrats to Gini and Alex on finding this paper recognized. Preprint here.
A journal paper has become acknowledged on prior constraints in tractable probabilistic designs, accessible on the papers tab. Congratulations Giannis!
Website link In the last 7 days of October, I gave a chat informally talking about explainability and moral accountability in artificial intelligence. Because of the organizers for your invitation.
Jonathan’s paper considers a lifted approached to weighted design integration, together with circuit building. Paulius’ paper develops a evaluate-theoretic standpoint on weighted model counting and proposes a method to encode conditional weights on literals analogously to conditional probabilities, which results in substantial general performance improvements.
Within the College of Edinburgh, he directs a analysis lab on synthetic intelligence, specialising from the unification of logic and device learning, having a current emphasis on explainability and ethics.
The paper discusses how to handle nested features and quantification in relational probabilistic graphical models.
I gave an invited tutorial the Bath CDT Art-AI. I lined latest traits and https://vaishakbelle.com/ long term developments on explainable device Finding out.
Convention website link Our work on symbolically interpreting variational autoencoders, in addition to a new learnability for SMT (satisfiability modulo principle) formulas bought approved at ECAI.