Sat 17 Jun 2023 10:40 - 11:00 at Magnolia 5 - DRAGSTERS: Session 1

We present dappl, a new probabilistic programming language to model decision making and exactly solve maximum expected utility problems (MEU). dappl is a functional language with support for first-class decision-making, rewards, and probabilistic uncertainty. Our goal in designing dappl is to (1) support scalable MEU reasoning, and (2) provide a flexible and expressive programming environment capable of representing complex real-world decision-making tasks. To accomplish these two goals we develop a new reasoning-via-compilation strategy for dappl. We reduce dappl MEU computation to a flexible branch-and-bound algorithm over compiled Boolean formulas weighted by the expectation semiring, then prove this reduction correct with respect to a denotational semantics. Furthermore, we demonstrate that our language is as expressive as known decision–theoretic probabilistic graphical models such as influence diagrams.

Sat 17 Jun

Displayed time zone: Eastern Time (US & Canada) change

09:00 - 11:00
09:00
20m
Talk
Matrix Decompositions over Database Joins
DRAGSTERS
Dan Olteanu University of Zurich, Nils Vortmeier Ruhr University Bochum, Dorde Zivanovic University of Oxford
09:20
20m
Talk
NASOQ: Numerically Accurate Sparsity-Oriented QP Solver
DRAGSTERS
Kazem Cheshmi McMaster University, Maryam Mehri Dehnavi University of Toronto
09:40
20m
Talk
UniSparse: An Intermediate Language and Compiler for General Sparse Format Customization
DRAGSTERS
Jie Liu Cornell University, Zhongyuan Zhao , Zijian Ding Peking University, Benjamin Brock Parallel Computing Lab (PCL), Intel, Hongbo Rong Intel Labs, Zhiru Zhang Cornell University, USA
10:00
20m
Talk
Unification as a means of completing partial data structures
DRAGSTERS
Joachim Kristensen University of Oslo, Robin Kaarsgaard University of Southern Denmark, Michael Kirkedal Thomsen University of Oslo & University of Copenhagen
10:20
20m
Talk
Formalizing DRAGSTERS
DRAGSTERS
Scott Kovach Stanford University
10:40
20m
Talk
Scaling Decision--Theoretic Probabilistic Programming Through Factorization
DRAGSTERS
Minsung Cho Northeastern University, Steven Holtzen Northeastern University