2022 Fall
Welcome to the homepage of CSE 645: Seminar in Languages (Fall 2022)!
General Information
Course description: We will read papers and discuss research ranging from high-level specifications (such as logic, rules, and sets) to algorithms and methods for efficient implementations of programming languages, with a wide range of applications.
Main topics planned for Fall 2022 will include Rule and query languages, Efficient inference and learning, and Probabilistic programming
Everyone is welcome. If you are enrolled in the class, you are expected to attend a majority of the meetings and present a paper.
Hours: Thursdays, 11:30AM-12:50PM ET on Zoom https://stonybrook.zoom.us/j/94298584108?pwd=OXRLemROdk8xOHY3NXFRV2xjQmhVUT09
Instructors: Annie Liu, CR Ramakrishnan, Michael Kifer, David Warren, and Paul Fodor.
Previous semesters: see menu to the left.
Schedule
8/25 Organizational meeting, and some examples of Datalog, negation, aggregation (count, sum)---transitive closure, different reachability queries, Barber paradox, win-not-win game, double-win game, and an opposite of double-win game.
9/1 Prof. Michael Kifer. Elements of Rule-based Knowledge Representation and Reasoning (Basics)
9/8 Prof. Michael Kifer. Elements of Rule-based Knowledge Representation and Reasoning (Advanced)
9/15 Yi will present: Khamis, Mahmoud Abo, Hung Q. Ngo, Reinhard Pichler, Dan Suciu, and Yisu Remy Wang. "Datalog in Wonderland." ACM SIGMOD Record 51, no. 2 (2022): 6-17. (summary of two papers below. thanks Tuncay for pointing this out explicitly)
Wang, Yisu Remy, Mahmoud Abo Khamis, Hung Q. Ngo, Reinhard Pichler, and Dan Suciu. "Optimizing Recursive Queries with Program Synthesis." arXiv preprint arXiv:2202.10390 (2022).
Abo Khamis, Mahmoud, Hung Q. Ngo, Reinhard Pichler, Dan Suciu, and Yisu Remy Wang. "Convergence of Datalog over (Pre-) Semirings." In Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pp. 105-117. 2022.
9/22 Doug will present: Brass, Stefan, and Mario Wenzel. "Performance Analysis and Comparison of Deductive Systems and SQL Databases." In Datalog, pp. 27-38. 2019. (slides by Doug incorporating some contents from slides by the authors, authors website with complete data)
9/29 Ranjani will present (but has to move to later, to 10/20): Arroyuelo, Diego, Aidan Hogan, Gonzalo Navarro, Juan L. Reutter, Javiel Rojas-Ledesma, and Adrián Soto. "Worst-case optimal graph joins in almost no space." In Proceedings of the 2021 International Conference on Management of Data, pp. 102-114. 2021. (slides by Ranjani)
Annie discussed Datalog extensions and libraries (including examples in ddlog and crepe; for more systems, see https://en.wikipedia.org/wiki/Datalog#Systems_implementing_Datalog) used in programming languages, and Programming with Logic Rules and Everything Else, Seamlessly, a revised version of https://arxiv.org/pdf/2205.15204.pdf from July 2022 (will update on arxiv).
10/6 Shivam will present: Wu, Jiacheng, Jin Wang, and Carlo Zaniolo. "Optimizing Parallel Recursive Datalog Evaluation on Multicore Machines." In Proceedings of the 2022 International Conference on Management of Data, pp. 1433-1446. 2022. (slides by Shivam)
10/13 Rucha will present: Wang, Zhe, Peng Xiao, Kewen Wang, Zhiqiang Zhuang, and Hai Wan. "Efficient datalog rewriting for query answering in TGD ontologies." IEEE Transactions on Knowledge and Data Engineering (2021). (slides by Rucha)
10/20 Yuheng will present (but has to move to later, to 11/17): Bellomarini, Luigi, Georg Gottlob, and Emanuel Sallinger. "The Vadalog system: Datalog-based reasoning for knowledge graphs." Proceedings of the VLDB Endowment, Vol. 11, No. 9. arXiv preprint arXiv:1807.08709 (2018). (slides by Yuheng)
Ranjani will present the paper she was to present on 9/29 (slides by Ranjani)
10/27 Jimmy will present work on graph representation learning:
Yan, Zuoyu, Tengfei Ma, Liangcai Gao, Zhi Tang, and Chao Chen. "Cycle Representation Learning for Inductive Relation Prediction." In Proceedings of the 39 th International Conference on Machine Learning, Baltimore, Maryland, USA, PMLR 162, 2022. (slides by Jimmy)
Zhao, Qi, Ze Ye, Chao Chen, and Yusu Wang. "Persistence enhanced graph neural network." In International Conference on Artificial Intelligence and Statistics, pp. 2896-2906. PMLR, 2020.
11/3 Hrushikesh will present: Wang, Huaduo, and Gopal Gupta. "FOLD-SE: Scalable Explainable AI." arXiv preprint arXiv:2208.07912 (2022). (improvement over two papers below)
Wang, Huaduo, and Gopal Gupta. "FOLD-R++: A Toolset for Automated Inductive Learning of Default Theories from Mixed Data." arXiv preprint arXiv:2110.07843 (2021).
Wang, Huaduo, and Gopal Gupta. "FOLD-RM: A Scalable and Efficient Inductive Learning Algorithm for Multi-Category Classification of Mixed Data." arXiv preprint arXiv:2202.06913 (2022).
11/10 Matthew will present (but will be moved to later): Fierens, Daan, Guy Van den Broeck, Joris Renkens, Dimitar Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens, and Luc De Raedt. "Inference and learning in probabilistic logic programs using weighted Boolean formulas." Theory and Practice of Logic Programming 15, no. 3 (2015): 358-401.
The Prize session of the Prolog Day Symposium will be overlapping with the seminar time. You can attend any sessions you are interested in https://prologyear.logicprogramming.org/PrologDay.html, by going to https://direct.u-paris.fr/lives, selecting tag "SAINT-GERMAIN" and then selecting "Amphitheater Polonovski".
11/17 Invited talk on probabilistic reasoning by Theresa Swift (but has to be postponed)
Yuheng will present the paper he was to present on 10/20 (slides by Yuheng)
11/24 Thanksgiving break
12/1 Yash will present: Lamport, Leslie. "Deconstructing the bakery to build a distributed state machine." Communications of the ACM 65, no. 9 (2022): 58-66. (marked version by Yash)
Below are topics with references:
Logic and knowledge base overview
slides for 9/1 and 9/8, weeks 2-3, by Prof. Kifer
Rule and query languages and efficient inference
papers for 9/15-10/22, weeks 4-9
Graph representation learning
papers for 10/27, week 10
Efficient learning with explainability
papers for 11/3, week 11
Probabilistic programming and efficient inference and learning
paper for 11/10 and invited talk, weeks 12-13
ICLP 2022 Invited talk by Fabrizio Riguzzi: Probabilistic Logic Programming: Semantics, Inference and Learning (slides)
Fadja, Dott Arnaud Nguembang, and Fabrizio Riguzzi. "Scalable Probabilistic Inductive Logic Programming for Big Data." 2019. (thesis presumably with more details about implementation; ask Fabrizio first)
Distributed computing
paper for 12/1, week 14