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2024年度
輪講（全体）
論文紹介など
- Automatic Fairness Testing of Machine Learning Models,ICTSS’20,中原,11/05.
- PyTorch公式チュートリアルのPyTorch入門,大橋,10/29.
- RPG: Rust Library Fuzzing with Pool-based Fuzz Target Generation and Generic Support,ICSE’24,松尾,10/29.
- Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning, EMNLP’23, 中里, 7/31.
- MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Search, ICSE’24, 趙, 7/9,19.
- Higher income, larger loan? monotonicity testing of machine learning models, ISSTA 2020; Automatic Fairness Testing of Machine Learning Models, ICTSS 2020; Diversity-aware fairness testing of machine learning classifiers through hashing-based sampling, Information and Software Technology 2024, 趙, 6/20.
- Black box fairness testing of machine learning models, ESEC/FSE 2019, 伊藤, 6/5.
- Attention Is All You Need, NIPS 2017, 中里, 5/22.
2023年度
輪講（全体）
論文紹介など
- グラフ圧縮技術の解説, 田中, 11/16.
- A SAT-Based Approach to Learn Explainable Decision Sets, IJCAR 2018, 中本, 9/14.
- Compiling finite linear CSP into SAT, CP 2006, 村田, 6/28.
- XGBoost: A Scalable Tree Boosting System, KDD 2016. 趙, 6/19.
- Constraint-Driven Explanations for Black Box ML Models, AAAI 2022. 伊藤, 6/13.
- Verifying properties of binarized deep neural networks, AAAI 2018. 小宮山, 6/7.
- Murxla: A Modular and Highly Extensible API Fuzzer for SMT Solvers. CAV 2022. 久保, 6/6.
- Fairify: Fairness Verification of Neural Networks, ICSE 2023. 趙, 6/6.
- 人狼知能エージェントについて, 八木, 6/5.
- ppSAT: Towards Two-Party Private SAT Solving. 31st USENIX Security Symposium (USENIX Security 22), 2022: 中里, 5/29.
- “Why Should I Trust You?”: Explaining the Predictions of Any Classifier (Ribeiro et al., NAACL 2016): 伊藤, 5/29.
2022年度
輪講（全体）
- テキスト: Donald E. Knuth. 2022. The Art of Computer Programming, Volume 4, Pre-Fascicle 7A: Constraint Satisfaction (very preliminary draft). Addison-Wesley Professional.
2021年度
輪講（全体）
2020年度
輪講（全体）