I gave a talk within the workshop on how the synthesis of logic and device Finding out, Specifically spots for instance statistical relational Studying, can empower interpretability.
Thinking about synthesizing the semantics of programming languages? We've got a different paper on that, approved at OOPSLA.
The paper tackles unsupervised system induction around mixed discrete-constant data, and is also recognized at ILP.
When you are attending NeurIPS this year, chances are you'll have an interest in checking out our papers that touch on morality, causality, and interpretability. Preprints can be found to the workshop page.
An article within the arranging and inference workshop at AAAI-18 compares two unique approaches for probabilistic scheduling via probabilistic programming.
I’ll be offering a chat in the convention on truthful and responsible AI inside the cyber Bodily units session. Thanks to Ram & Christian to the invitation. Backlink to party.
We have now a completely new paper recognized on learning optimum linear programming objectives. We get an “implicit“ hypothesis building strategy that yields wonderful theoretical bounds. Congrats to Gini and Alex on receiving this paper approved. Preprint below.
I gave a seminar on extending the expressiveness of probabilistic relational products with initially-buy functions, https://vaishakbelle.com/ such as common quantification in excess of infinite domains.
Recently, he has consulted with main financial institutions on explainable AI and its impact in monetary institutions.
While in the paper, we exploit the XADD knowledge framework to conduct probabilistic inference in combined discrete-ongoing spaces proficiently.
Paulius' Focus on algorithmic approaches for randomly creating logic applications and probabilistic logic programs has been recognized to your ideas and practise of constraint programming (CP2020).
The paper discusses how to deal with nested features and quantification in relational probabilistic graphical products.
The primary introduces a primary-get language for reasoning about probabilities in dynamical domains, and the 2nd considers the automatic solving of likelihood challenges laid out in natural language.
Our work (with Giannis) surveying and distilling strategies to explainability in device Studying has been approved. Preprint listed here, but the ultimate Model will likely be on the web and open access shortly.