Workshops and Tutorials on AI S&P in Security, ML, CV, NLP, and IR & RecSys venues.
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- Artificial Intelligence and Security (CCS 2008-present)
- Deep Learning and Security (Oakland 2018-present)
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- Socially Responsible Machine Learning (ICLR 2022)
- Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (ICLR 2022)
- Security and Safety in Machine Learning Systems (ICLR 2021)
- Robust and Reliable Machine Learning in the Real World (ICLR 2021)
- Towards Trustworthy ML: Rethinking Security and Privacy for ML (ICLR 2020)
- Safe Machine Learning: Specification, Robustness and Assurance (ICLR 2019)
- Theory and Practice of Differential Privacy (ICML 2022)
- New Frontiers in Adversarial Machine Learning (ICML 2022)
- A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning (ICML 2021)
- Socially Responsible Machine Learning (ICML 2021)
- Uncertainty & Robustness in Deep Learning (ICML 2021, ICML 2020)
- Security and Privacy of Machine Learning (ICML 2019)
- Privacy in Machine Learning (NeurIPS 2021)
- Dataset Curation and Security (NeurIPS 2020)
- Security in Machine Learning (NeurIPS 2018)
- Machine Learning and Computer Security (NeurIPS 2017)
- Adversarial Training (NeurIPS 2016)
- Reliable Machine Learning in the Wild (NeurIPS 2016)
- Adversarial Learning Methods for Machine Learning and Data Mining (KDD 2022, KDD 2021, KDD 2020, KDD 2019)
- Artificial Intelligence Safety (AAAI 2019-2022)
- Practical Deep Learning in the Wild (AAAI 2022)
- Adversarial Machine Learning and Beyond (AAAI 2022)
- Towards Robust, Secure and Efficient Machine Learning (AAAI2021)
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- Adversarial Robustness - Theory and Practice (NeurIPS 2018)
- Adversarial Robustness in Deep Learning: From Practices to Theories (KDD 2021)
- Adversarial Attacks and Defenses: Frontiers, Advances and Practice (KDD 2020)
- Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications (ICDM 2020)
- Adversarial Machine Learning for Good (AAAI 2022)
- Adversarial Machine Learning (AAAI 2018)
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- The Art of Robustness: Devil and Angel in Adversarial Machine Learning (CVPR 2022)
- The Bright and Dark Sides of Computer Vision: Challenges and Opportunities for Privacy and Security (CVPR 2021, ECCV 2020, CVPR 2019, CVPR 2018, CVPR 2017 )
- Adversarial Robustness in the Real World (ECCV 2022, ICCV 2021, CVPR 2021, ECCV 2020, CVPR 2020, CVPR 2019)
- Adversarial Learning for Multimedia (ACMMM 2021)
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- Adversarial Machine Learning in Computer Vision (CVPR 2021)
- Practical Adversarial Robustness in Deep Learning: Problems and Solutions (CVPR 2021)
- Adversarial Robustness of Deep Learning Models (ECCV 2020)
- Deep Learning for Privacy in Multimedia (ACMMM 2020)
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- Analyzing and Interpreting Neural Networks for NLP (EMNLP 2021, EMNLP 2020, ACL 2019, EMNLP 2018)
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- Robustness and Adversarial Examples in Natural Language Processing (EMNLP 2021)
- Deep Adversarial Learning for NLP (NAACL 2019)
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- Adversarial Machine Learning for Recommendation and Search (CIKM 2021)
- Online Misinformation- and Harm-Aware Recommender Systems (RecSys 2021, RecSys 2020)
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- Adversarial Machine Learning in Recommender Systems (ECIR 2021, RecSys 2020, WSDM 2020)