Apr 23 at 12pm (NSH 3305) -- Yexiang Xue (Purdue) -- Vertical Reasoning Enhanced Learning, Generation and Scientific Discovery

[Victor Akinwande]

Dear all,

We look forward to seeing you next Tuesday (04/23) from 12:00-1:00 PM (ET)
for the next talk of this semester's CMU AI Seminar, sponsored by SambaNova
Systems (https://sambanova.ai). The seminar will be held in NSH 3305 with
pizza provided and will be streamed on Zoom.

To learn more about the seminar series or to see the future schedule,
please visit the seminar website (http://www.cs.cmu.edu/~aiseminar/).

Next Tuesday (04/23),  Yexiang Xue (Purdue) will be giving a talk titled
"Vertical Reasoning Enhanced Learning, Generation and Scientific Discovery".

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*Talk Abstract: *
Automated reasoning and machine learning are two fundamental pillars of
artificial intelligence. Despite much recent progress, the full integration
of reasoning and learning is beyond reach. This talk presents three cases
where integrated vertical reasoning significantly enhances learning. Our
first case is in neural generation, where state-of-the-art models struggle
to generate pleasing images while satisfying complex specifications. We
introduce Spatial Reasoning INtegrated Generator (SPRING). SPRING embeds a
spatial reasoning module inside the deep generative network to reason
object locations. This embedded approach guarantees constraint
satisfaction, offers interpretability, and facilitates zero-shot transfer
learning. Our second case is in AI-driven scientific discovery, where we
embed vertical reasoning to expedite symbolic regression. Vertical
reasoning builds from reduced models that involve a subset of variables (or
processes) to full models, inspired by the human scientific approach.
Vertical discovery outperforms horizontal ones at discovering equations
involving many variables and complex processes, especially in learning PDEs
in computational materials science. In the third case, we demonstrate that
vertical reasoning enables constant approximation guarantees in solving
Satisfiable Modulo Counting (SMC). SMC involves model counting as
predicates in Boolean satisfiability. It encompasses many problems that
require both symbolic decision-making and statistical reasoning, e.g.,
stochastic optimization, hypothesis testing, solving quantal-response
leader-follower games, and learning (inverse reinforcement learning) with
provable guarantees. Using vertical reasoning that streamlines XOR
constraints, our proposed XOR-SMC reduces highly intractable SMC problems
into solving satisfiability instances, while obtaining a constant
approximation guarantee.


*Speaker Bio: *Dr. Yexiang Xue is an assistant professor in the Department
of Computer Science, Purdue University. The goal of Dr. Xue’s research is
to bridge large-scale constraint-based reasoning with state-of-the-art
machine learning techniques to enable intelligent agents to make optimal
decisions in high-dimensional and uncertain real-world applications. More
specifically, Dr Xue’s research focuses on scalable and accurate
probabilistic reasoning, statistical modeling of data, and robust
decision-making under uncertainty. His work is motivated by key problems
across multiple scientific domains, ranging from artificial intelligence,
machine learning, renewable energy, materials science, crowdsourcing,
citizen science, urban computing, ecology, to behavioral econometrics. Dr.
Xue obtained his PhD from Cornell, supervised by Professor Bart Selman and
Professor Carla Gomes, before joining Purdue in 2018. Recently, Dr. Xue has
been focusing on developing cross-cutting computational methods, with an
emphasis in the areas of computational sustainability and AI-driven
scientific discovery.
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*In person: NSH 3305*
*Zoom Link:
 https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09
<https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09>*


- Victor & Asher
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