Drew, Dave, Larissa and I experienced the chance to discuss the motivatons and foundations for instigating the new investigation topic of Experiential AI inside a 90 minute converse.
Weighted model counting frequently assumes that weights are only specified on literals, frequently necessitating the need to introduce auxillary variables. We consider a completely new solution dependant on psuedo-Boolean features, leading to a more typical definition. Empirically, we also get SOTA effects.
Will likely be Talking for the AIUK function on principles and observe of interpretability in device learning.
He has produced a job out of carrying out investigate around the science and know-how of AI. He has published close to one hundred twenty peer-reviewed article content, won greatest paper awards, and consulted with financial institutions on explainability. As PI and CoI, he has secured a grant money of close to 8 million lbs.
We look at the concern of how generalized programs (ideas with loops) can be considered proper in unbounded and constant domains.
The write-up, to seem in The Biochemist, surveys a number of the motivations and techniques for making AI interpretable and accountable.
The problem we deal with is how the educational needs to be defined when there is lacking or incomplete knowledge, leading to an account based on imprecise probabilities. Preprint in this article.
A journal paper continues to be recognized on prior constraints in tractable probabilistic versions, available about the papers tab. Congratulations Giannis!
Url In the last week of Oct, I gave a talk informally speaking about explainability and ethical accountability in artificial intelligence. Because of the organizers for the invitation.
From the paper, we exploit https://vaishakbelle.com/ the XADD facts framework to complete probabilistic inference in blended discrete-continuous spaces competently.
He has served over the senior program committee/region chair of main AI conferences, co-chaired the ML monitor at KR, amid Other folks, and as PI and CoI secured a grant profits of near eight million kilos.
The framework is applicable to a significant course of formalisms, which includes probabilistic relational designs. The paper also studies the synthesis trouble in that context. Preprint below.
I gave an invited tutorial the Bath CDT Art-AI. I covered current trends and long term trends on explainable machine learning.
Convention link Our Focus on symbolically interpreting variational autoencoders, as well as a new learnability for SMT (satisfiability modulo idea) formulas bought acknowledged at ECAI.