2017 Fall
Welcome to the homepage of CSE645 (Fall 2017) Seminar in Languages!
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, with applications in semantic web, program analysis, security, and services. If you are enrolled in the class, you must attend at least 50% of the meetings and to present a paper during the semester.
Instructors: Annie Liu, CR Ramakrishnan, Michael Kifer, David Warren, and Paul Fodor (contact: paul.fodor@stonybrook.edu).
Hours: Thursdays, 11:30PM-1:00PM, in New Computer Science Department room 220.
Schedule
08/31/2017 Organizational meeting and summary of the Prolog 2017 contest: https://sites.google.com/site/prologcontest2017/
09/07/2017 Chris Kane will discuss: Hawk: The blockchain model of cryptography and privacy-preserving smart contracts. Kosba, Ahmed and Miller, Andrew and Shi, Elaine and Wen, Zikai and Papamanthou, Charalampos. IEEE Symposium on Security and Privacy, 2016.
http://www.cs.umd.edu/sites/default/files/scholarly_papers/Kosba.pdf
09/14/2017 Saksham will present An Integrated Solver for Optimization Problems [Short Slides] [Detailed Slides]
09/21/2017 Saksham will continue his presentation from 9/14
09/28/2017 Tim will present Variational Bayes via propositionalized probability computation in PRISM
Taisuke Sato, Yoshitaka Kameya, Kenichi Kurihara. In Annals of Mathematics and Artificial Intelligence, November 2008, Volume 54, Issue 1–3, pp 135–158.
https://link.springer.com/article/10.1007/s10472-009-9135-8
with general background proofs for EM, Variational EM, as presented by Matthew J. Beal in his thesis:
Thesis: Matthew J. Beal, Variational Algorithms for Approximate Bayesian Inference https://www.cse.buffalo.edu//faculty/mbeal/thesis/
https://www.cse.buffalo.edu/faculty/mbeal/thesis/beal03_2.pdf
10/05/2017 Wendy will present Reasoning About Entailment With Neural Attention
Tim Rocktaschel, University College London
Edward Grefenstette & Karl Moritz Hermann Google DeepMind
https://arxiv.org/abs/1509.06664
https://rockt.github.io/publications.html
10/12/2017 Ashkan will present "Optimization programming languages", Roland Martin.
https://www.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_ALGO/pdf/AlgMod/AlgMod_OPL.pdf
10/19/2017 Shweta Bharti will present Pyomo: Modeling and Solving Mathematical Programs in Python [Slides][Some Slides]
http://mpc.zib.de/index.php/MPC/article/viewFile/59/30
10/26/2017 Tiantian will present ATHENA: https://dl.acm.org/citation.cfm?id=2994536
11/02/2017 Xinxin Huo will present "Convex Optimization in Julia. Madeleine Udell, Karanveer Mohan, David Zeng. In 2014 First Workshop for High Performance Technical Computing in Dynamic Languages.
http://dl.acm.org/citation.cfm?id=2688223
11/09/2017 Matthew will present PPDB: The Paraphrase Database
Juri Ganitkevitch, Benjamin Van Durme, Chris Callison-Burch, Johns Hopkins University, University of Pennsylvania
http://www.aclweb.org/anthology/N13-1092
11/16/2017 Sarthak will present "Human-level concept learning through probabilistic program induction", by Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum, Science 11 Dec 2015: Vol. 350, Issue 6266, pp. 1332-1338
https://pdfs.semanticscholar.org/2307/8c79c0fc425653aeafcee2ddd01210254658.pdf
11/23/2017 Thanksgiving break
11/30/2017 Minghui will present PySP: modeling and solving stochastic programs in Python
12/07/2017 Amit will present TensorLog: A Differentiable Deductive Database
William W. Cohen Department of Machine Learning Carnegie Mellon University
https://arxiv.org/abs/1605.06523
Stand discussions:
Papers
We will select papers from the following list (don't have to cover all) and possibly other interesting ones as they come up.
Recursive Neural Networks Can Learn Logical Semantics
Samuel R. Bowman, Stanford Linguistics
Christopher Potts, Stanford NLP Group
Christopher D. Manning, Stanford Computer Science, 2014.
https://arxiv.org/abs/1406.1827
TensorLog: A Differentiable Deductive Database
William W. Cohen Department of Machine Learning Carnegie Mellon University
https://arxiv.org/abs/1605.06523
PPDB: The Paraphrase Database
Juri Ganitkevitch, Benjamin Van Durme, Chris Callison-Burch, Johns Hopkins University, University of Pennsylvania
http://www.aclweb.org/anthology/N13-1092
Reasoning About Entailment With Neural Attention
Tim Rocktaschel, University College London
Edward Grefenstette & Karl Moritz Hermann Google DeepMind
https://arxiv.org/abs/1509.06664
https://rockt.github.io/publications.html
Learning Knowledge Base Inference with Neural Theorem Provers
Tim Rocktaschel and Sebastian Riedel
https://rockt.github.io/publications.html
Lifted Rule Injection for Relation Embeddings
Thomas Demeester, Tim Rocktaschel and Sebastian Riedel
https://rockt.github.io/publications.html
Computing Loops With at Most One External Support Rule. Xiaoping Chen, Jianmin Ji, Fangzhen Lin.
KR 2008 paper link: https://pdfs.semanticscholar.org/72d5/cfada8b0ade26a89cdf22e308b3a1a5e36a8.pdf
Slides link: http://peace.eas.asu.edu/iclp13/presentations/Computing_Loops_with_At_Most_One_External_Support_Rule.pdf
1) Pyomo: Modeling and Solving Mathematical Programs in Python [Slides][Some Slides]
http://mpc.zib.de/index.php/MPC/article/viewFile/59/30
2) PySP: modeling and solving stochastic programs in Python
3) JuMP: A Modeling Language for Mathematical Optimization [Some Slides]
4) Solving mixed integer linear and nonlinear problems using the SCIP Optimization Suite
Convex Optimization in Julia.
Madeleine Udell, Karanveer Mohan, David Zeng. In 2014 First Workshop for High Performance Technical Computing in Dynamic Languages.
http://dl.acm.org/citation.cfm?id=2688223
A short introduction to OPL - Optimization Programming Language.
Roland Martin. Report 2002.
https://www.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_ALGO/pdf/AlgMod/AlgMod_OPL.pdf
Modelica - a general object-oriented language for continuous and discrete-event system modeling and simulation
P. Fritzson, P. Bunus. In Proceedings 35th Annual Simulation Symposium. SS 2002.
http://ieeexplore.ieee.org/abstract/document/1000174/
Modeling and optimization with Optimica and JModelica.org—Languages and tools for solving large-scale dynamic optimization problems
J. Åkesson, K-E. ÅrzénaM.Gäfvert, T. Bergdahl, H.Tummescheit
http://www.sciencedirect.com/science/article/pii/S009813540900283X
Model Development, Solution, and Analysis in Global Optimization
János D. Pintér. In Global Optimization — Selected Case Studies 2002.
http://www.mat.univie.ac.at/~neum/glopt/mss/Pin02.pdf
A Benders approach for the constrained minimum break problem
Rasmus V. Rasmussen and Michael A. Trick. In European Journal of Operational Research , 177, 1, 198-213. 2005.
http://mat.gsia.cmu.edu/trick/benders.pdf