Foundations for Robust and Minimally-Supervised Learning Systems

背景机器学习是一种通过从历史经验中学习并通过其泛化能力推断新数据属性来开发软件的技术，它在很大程度上受到大数据可用性的推动。然而，人类注释者对大数据进行标记的成本很高，而且机器学习的泛化能力往往容易受到对抗性攻击和分布外数据的影响。例如，特斯拉2020年的报告中提到，附着在停车标志上的一个微小的对抗性补丁可以完全欺骗自动驾驶系统，尽管这些系统是用6年来收集的多达30亿英里的真实的数据训练的。目标.拟议研究计划的长期目标是通过开发具有可证明保证和实际应用的通用，原则性和可扩展算法，从鲁棒性和自我监督的角度推进机器学习。因此，目标包括：1）针对不受限制的对抗性示例和分布外数据开发新的鲁棒模型，并将鲁棒性与公平性，隐私性和可解释性联系起来，以实现可信的机器学习; 2）缩小监督学习和自监督学习之间的性能差距;以及3）将所提出的鲁棒和自监督框架应用于现实世界的任务，包括3D重建，文本分析和Deepfake检测。影响和奖励。开发的强大方法预计将作为第一个基线，以鼓励开发针对更广泛类别的对抗性攻击的新防御。该技术将有可能加速自动驾驶汽车的研发，使人工智能在加拿大安全、可靠、透明和值得信赖。此外，这项研究将大大降低加拿大高科技公司的数据标签成本。拟议的研究计划将改善机器人导航的实时3D感知，Metaverse的AR/VR，存在错别字的文本分析以及YouTube和Twitter上的虚假视频检测的最新技术。学生将学习必要的技能，进行研究计划，科学写作，批判性思维和编码。在课程结束时，学生将成为机器学习专家，他们将在顶尖大学的工业研究科学家职位或学术研究职位上处于有利地位。

Background. Machine learning, a technology that develops software through learning from historical experience and inferencing the properties of new data by its generalization ability, has been largely driven by the availability of big data. However, labeling of big data by human annotators is costly, and machine learning generalization ability is often susceptible to adversarial attacks and out-of-distribution data. For example, a tiny adversarial patch attached to a stop sign could completely fool autopilot systems, even though the systems were trained with as many as 3-billion-mile real data collected over 6 years, quoted from Tesla's 2020 report. Objectives. The long-term goal of the proposed research program is to advance machine learning from the robustness and self-supervision perspectives, by developing generic, principled, and scalable algorithms with provable guarantees and practical applications. Therefore, the objectives include: 1) developing new robust models against unrestricted adversarial examples and out-of-distribution data and connecting robustness with fairness, privacy and interpretability towards trustworthy machine learning; 2) closing the performance gap between supervised and self-supervised learning; and 3) applying the proposed robust and self-supervised framework to the real-world tasks, including 3D reconstruction, textual analysis, and Deepfake detection. Impacts and Reward. The developed robust method is expected to serve as the first baseline to encourage the development of new defenses against a broader class of adversarial attacks. The technology will potentially accelerate the research and development of autonomous cars, making AI safe, reliable, transparent, and trustworthy in Canada. Moreover, this research will significantly reduce the cost of data labeling for high-tech companies in Canada. The proposed research program will improve the state-of-the-art of real-time 3D perception for robotics navigation, AR/VR for Metaverse, textual analysis in the presence of typos, and fake video detection on YouTube and Twitter. Students will learn the necessary skills to carry out a research plan, scientific writing, critical thinking, and coding. By the end of their program, the students will become machine learning experts who are well-positioned for industrial research scientist positions or academic research positions in top universities.