計算機構論 II （2023年度Sセメスター）
授業の形式と目的
本講義では，プログラミング言語・並列/分散プログラミングなどにまつわる最近のトピックスについて輪講形式で学びます．今年度は「確率的プログラミング言語」をテーマとし，ソフトウェア科学・工学の広範な分野にまたがる下記の文献リストから，各人が興味があるものを選び輪講形式で発表します（文献リストは変更される可能性があります）．
初回にガイダンスを行い，各人の担当部分の割当てを決めます． なお、2回目は森畑が文献1を紹介する予定です．
基本情報
担当教員：森畑明昌（総合文化研究科）　morihata_AT_graco.c.u-tokyo.ac.jp
曜限：月曜3限（13:15-14:45）
原則としてオンライン開講の予定です．
※履修希望者は4/16までに以下のフォームに記入・提出してください．
履修登録フォーム
授業計画（敬称略）
文献リスト
  1. Probabilistic programming (FOSE 2014: Future of Software Engineering, Proceedings.)

  2. Probabilistic programming in Python using PyMC3 (PeerJ Computer Science, 2016)
  3. Stan: A Probabilistic Programming Language (Journal of Statistical Software, 2017)
  4. Simple, Distributed, and Accelerated Probabilistic Programming (Advances in Neural Information Processing Systems, NeurIPS 2018)
  5. Conditioning in Probabilistic Programming (ACM Transactions on Programming Languages and Systems, 2018)
  6. Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference (International Conference on Probabilistic Graphical Models, 2019)
  7. Etalumis: Bringing probabilistic programming to scientific simulators at scale (SC '19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis)
  8. Gen: a general-purpose probabilistic programming system with programmable inference (PLDI 2019: Proceedings of International Conference on Programming Language Design and Implementation)
  9. Applying Probabilistic Programming to Affective Computing (IEEE Transactions on Affective Computing, 2019)
  10. Storm: program reduction for testing and debugging probabilistic programming systems (ESEC/FSE 2019: Proceedings of Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering)
  11. Universal probabilistic programming offers a powerful approach to statistical phylogenetics (Communications Biology, 2020)
  12. Reactive probabilistic programming (PLDI 2020: Proceedings of International Conference on Programming Language Design and Implementation)
  13. Privug: Using Probabilistic Programming for Quantifying Leakage in Privacy Risk Analysis (Computer Security – ESORICS 2021)
  14. PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming (Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, 2021)
Akimasa Morihata. Feb., 2023