Drew, Dave, Larissa and I had the chance to discuss the motivatons and foundations for instigating the new investigation topic of Experiential AI in a 90 minute converse.
I are going to be supplying a tutorial on logic and learning with a focus on infinite domains at this year's SUM. Link to event here.
I gave a talk entitled "Views on Explainable AI," at an interdisciplinary workshop concentrating on creating have confidence in in AI.
I attended the SML workshop while in the Black Forest, and mentioned the connections amongst explainable AI and statistical relational Studying.
An post at the scheduling and inference workshop at AAAI-eighteen compares two unique approaches for probabilistic scheduling by the use of probabilistic programming.
I gave a talk on our current NeurIPS paper in Glasgow though also masking other methods at the intersection of logic, Discovering and tractability. Because of Oana for your invitation.
We've got a completely new paper accepted on Understanding best linear programming targets. We take an “implicit“ hypothesis building technique that yields awesome theoretical bounds. Congrats to Gini and Alex on obtaining this paper acknowledged. Preprint listed here.
I gave a seminar on extending the expressiveness of probabilistic relational designs with very first-purchase attributes, like universal quantification over infinite domains.
Hyperlink In the final week of October, I gave a chat informally talking about explainability and moral accountability in artificial intelligence. Due to the organizers to the invitation.
, to help systems to master a lot quicker and much more correct versions of the globe. We have an interest in establishing computational frameworks that can easily reveal their selections, modular, re-usable
Prolonged abstracts of our NeurIPS paper (on PAC-Mastering in very first-order logic) and the journal paper on abstracting probabilistic styles was acknowledged to KR's lately posted exploration monitor.
The paper discusses how to manage nested functions and quantification in relational probabilistic graphical types.
The very first introduces a first-get language for reasoning about https://vaishakbelle.com/ probabilities in dynamical domains, and the 2nd considers the automated solving of likelihood challenges laid out in natural language.
Convention hyperlink Our Focus on symbolically interpreting variational autoencoders, in addition to a new learnability for SMT (satisfiability modulo theory) formulation obtained acknowledged at ECAI.