Campus Event Calendar
Event Entry
What and Who
From Simple Inference to Complex Probabilistic Reasoning
Antonio Vergari
UCLA
Talk
AG 1, AG 2, AG 3, INET, AG 4, AG 5, SWS, RG1, MMCI
Expert Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Wednesday, 24 February 2021
17:00
60 Minutes
E1 1
412
Saarbrücken
Abstract
Probabilistic reasoning is generally considered to be the
framework-of-choice to enable and support decision making under
uncertainty in real-world scenarios. Ideally, we would like a
probabilistic ML system that is deployed in the wild to be able to i)
allow humans (or other AI agents) to pose arbitrary and articulated
queries, that is questions about states of the world; ii) to provide
guarantees on their results; iii) to deal with complex, heterogeneous
and potentially structured data and, moreover iv) to support chaining
several inference steps together. In this talk, I will argue that the
above desiderata are still unmet in the current landscape of
probabilistic ML. Even the most prominent paradigm nowadays, deep
generative modeling, is able to provide only a shallow, simplistic, form
of inference and struggles when dealing with complex queries or data. I
will then delineate how my past and current research aimed at closing
this gap. Specifically, I will touch some recent works investigating
principled frameworks within dealing with complex tasks such as
reasoning about the behavior of classifiers or dealing with algebraic
constraints over heterogeneous data can be done elegantly and
efficiently. Lastly I will talk about some future research perspectives:
extending these complex probabilistic reasoning routines to interactive
and relational settings while allowing for approximations with guarantees.
framework-of-choice to enable and support decision making under
uncertainty in real-world scenarios. Ideally, we would like a
probabilistic ML system that is deployed in the wild to be able to i)
allow humans (or other AI agents) to pose arbitrary and articulated
queries, that is questions about states of the world; ii) to provide
guarantees on their results; iii) to deal with complex, heterogeneous
and potentially structured data and, moreover iv) to support chaining
several inference steps together. In this talk, I will argue that the
above desiderata are still unmet in the current landscape of
probabilistic ML. Even the most prominent paradigm nowadays, deep
generative modeling, is able to provide only a shallow, simplistic, form
of inference and struggles when dealing with complex queries or data. I
will then delineate how my past and current research aimed at closing
this gap. Specifically, I will touch some recent works investigating
principled frameworks within dealing with complex tasks such as
reasoning about the behavior of classifiers or dealing with algebraic
constraints over heterogeneous data can be done elegantly and
efficiently. Lastly I will talk about some future research perspectives:
extending these complex probabilistic reasoning routines to interactive
and relational settings while allowing for approximations with guarantees.
https://cs-uni-saarland-de.zoom.us/j/98785909683?pwd=MEdPR3VpTUVmb25JbUtRTnRYQkUwdz09
Contact
Mona Linn
+49 681 302 70157
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Mona Linn, 02/18/2021 14:00 -- Created document.