- Privacy and Security in ML (PriSec-ML)
- Machine Learning Security (MLSec)
- Seminars on Security & Privacy in Machine Learning (ML S&P)
- AI Security and Privacy (AISP) (in Chinese)
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- Artificial Intelligence and Security (CCS 2008-present)
- Deep Learning and Security (Oakland 2018-present)
- Dependable and Secure Machine Learning (DSN 2018-present)
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Trustworthy and Reliable Large-Scale Machine Learning Models (ICLR 2023)
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Backdoor Attacks and Defenses in Machine Learning (ICLR 2023)
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Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (ICLR 2022)
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Security and Safety in Machine Learning Systems (ICLR 2021)
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Robust and Reliable Machine Learning in the Real World (ICLR 2021)
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Towards Trustworthy ML: Rethinking Security and Privacy for ML (ICLR 2020)
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Safe Machine Learning: Specification, Robustness and Assurance (ICLR 2019)
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New Frontiers in Adversarial Machine Learning (ICML 2022-2023)
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Theory and Practice of Differential Privacy (ICML 2021-2022)
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Uncertainty & Robustness in Deep Learning (ICML 2020-2021)
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A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning (ICML 2021)
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Security and Privacy of Machine Learning (ICML 2019)
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Socially Responsible Machine Learning (NeurIPS 2022, ICLR 2022, ICML 2021)
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ML Safety (NeurIPS 2022)
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Privacy in Machine Learning (NeurIPS 2021)
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Dataset Curation and Security (NeurIPS 2020)
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Security in Machine Learning (NeurIPS 2018)
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Machine Learning and Computer Security (NeurIPS 2017)
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Adversarial Training (NeurIPS 2016)
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Reliable Machine Learning in the Wild (NeurIPS 2016)
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Adversarial Learning Methods for Machine Learning and Data Mining (KDD 2019-2022)
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Privacy Preserving Machine Learning (FOCS 2022, CCS 2021, NeurIPS 2020, CCS 2019, NeurIPS 2018)
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SafeAI (AAAI 2019-2022)
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Privacy-Preserving Artificial Intelligence (AAAI 2020-2023)
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Practical Deep Learning in the Wild (AAAI 2022)
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Adversarial Machine Learning and Beyond (AAAI 2022)
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Towards Robust, Secure and Efficient Machine Learning (AAAI2021)
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AISafety (IJCAI 2019-2022)
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- Adversarial Machine Learning on Computer Vision: Art of Robustness (CVPR 2023, CVPR 2022)
- Secure and Safe Autonomous Driving (CVPR 2023)
- Adversarial Robustness in the Real World (ECCV 2022, ICCV 2021, CVPR 2021, ECCV 2020, CVPR 2020, CVPR 2019)
- 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)
- Responsible Computer Vision (ECCV 2022)
- Safe Artificial Intelligence for Automated Driving (ECCV 2022)
- Adversarial Learning for Multimedia (ACMMM 2021)
- Adversarial Machine Learning towards Advanced Vision Systems (ACCV 2022)
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- BlackboxNLP (EMNLP 2022, EMNLP 2021, EMNLP 2020, ACL 2019, EMNLP 2018)
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- Online Misinformation- and Harm-Aware Recommender Systems (RecSys 2021, RecSys 2020)
- Adversarial Machine Learning for Recommendation and Search (CIKM 2021)
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- Quantitative Reasoning About Data Privacy in Machine Learning (ICML 2022)
- Foundational Robustness of Foundation Models (NeurIPS 2022)
- Adversarial Robustness - Theory and Practice (NeurIPS 2018)
- Towards Adversarial Learning: from Evasion Attacks to Poisoning Attacks (KDD 2022)
- 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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- 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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- Robustness and Adversarial Examples in Natural Language Processing (EMNLP 2021)
- Deep Adversarial Learning for NLP (NAACL 2019)
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- Adversarial Machine Learning in Recommender Systems (ECIR 2021, RecSys 2020, WSDM 2020)
- Special Track on Safe and Robust AI (AAAI 2023)
- Special Session on Adversarial Learning for Multimedia Understanding and Retrieval (ICMR 2022)
- Special Session on Adversarial Attack and Defense (APSIPA 2022)
- Special Session on Information Security meets Adversarial Examples (WIFS 2019)