Special Seminar: Antonio Vergari "From Simple Inference to Complex Probabilistic Reasoning"

From Simple Inference to Complex Probabilistic Reasoning

Antonio Vergari
UCLA
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Thursday, 11 March at 18:00
via Zoom: request the link by email [email protected]
Note! the link will be sent to the CS staff separately every day.

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.

Bio: Antonio Vergari is currently a postdoc in the StarAI Lab lead by Guy Van den Broeck at UCLA working on advanced probabilistic reasoning and learning on deep representations. Previously, he did a postdoc at the MPI-IS in Tuebingen where he worked with Isabel Valera on automating machine learning via tractable probabilistic models. He obtained a PhD in Computer Science and Mathematics at the University of Bari, Italy. He organized the Tractable Probabilistic Modeling Workshop at ICML2019, the Tractable PRobabilistic Inference MEeting (T-PRIME) at NeurIPS 2019 and presented a series of tutorials on modern probabilistic reasoning and models at UAI 2019, AAAI 2020, ECAI 2020. He will organize a Dagsthul Seminar on "New Trends in Tractable Probabilistic Inference" in 2022 with Prof. Kristian Kersting and Prof. Max Welling.