I gave a chat, entitled "Explainability like a company", at the above celebration that discussed expectations pertaining to explainable AI and how may be enabled in purposes.
Enthusiastic about synthesizing the semantics of programming languages? Now we have a new paper on that, accepted at OOPSLA.
The paper tackles unsupervised program induction about mixed discrete-ongoing info, and is approved at ILP.
He has made a occupation outside of performing exploration on the science and technological innovation of AI. He has posted close to one hundred twenty peer-reviewed content, received very best paper awards, and consulted with financial institutions on explainability. As PI and CoI, he has secured a grant money of near 8 million pounds.
Gave a talk this Monday in Edinburgh within the ideas & apply of machine learning, masking motivations & insights from our study paper. Essential inquiries raised integrated, how you can: extract intelligible explanations + modify the product to suit shifting wants.
I’ll be giving a talk within the conference on truthful and responsible AI from the cyber physical programs session. Thanks to Ram & Christian to the invitation. Link to occasion.
We have a fresh paper approved on Discovering optimum linear programming goals. We just take an “implicit“ speculation design approach that yields good theoretical bounds. Congrats to Gini and Alex on receiving https://vaishakbelle.com/ this paper approved. Preprint below.
A journal paper is accepted on prior constraints in tractable probabilistic products, out there to the papers tab. Congratulations Giannis!
Url In the last 7 days of October, I gave a chat informally discussing explainability and moral duty in synthetic intelligence. Because of the organizers with the invitation.
Jonathan’s paper considers a lifted approached to weighted model integration, like circuit design. Paulius’ paper develops a measure-theoretic perspective on weighted design counting and proposes a method to encode conditional weights on literals analogously to conditional probabilities, which results in significant functionality advancements.
Paulius' work on algorithmic techniques for randomly producing logic applications and probabilistic logic applications continues to be accepted to the concepts and practise of constraint programming (CP2020).
The framework is applicable to a significant class of formalisms, together with probabilistic relational types. The paper also studies the synthesis dilemma in that context. Preprint right here.
If you are attending AAAI this year, you might be interested in looking at our papers that touch on fairness, abstraction and generalized sum-solution troubles.
Our paper on synthesizing options with loops from the existence of probabilistic sound, acknowledged the journal of approximate reasoning, has also been accepted to the ICAPS journal track. Preprint to the entire paper below.