Theoretical Foundations for Safe, Adaptive and Interpretable Machine Learning

拟议的研究是在为机器学习发展理论基础的领域。机器学习技术正被应用于各种日常生活系统中，对工业、科学和社会的影响越来越大。这包括商业用途，如推荐系统或个人设备，但也包括关键应用，如自动驾驶车辆、脑机接口或医疗辅助。由于机器学习的实践取得了巨大的成功，学习方法被应用于大量的应用领域，因此对这些方法进行扎实和形式化的分析和理解变得越来越重要。目前许多最成功的学习方法(特别是基于深度学习的方法)都有“黑盒”的性质。虽然他们在基于大量数据构建准确预测方面取得了巨大成功，但越来越多的社会对数据驱动的工具以不断扩大但不受控制的方式影响人们的生活感到不安。事实上，令人遗憾的是，我们对这些方法为什么和如何工作，什么会影响它们的结果，人们可以为它们的结果提供什么类型的正确性保证，以及我们如何解释机器学习系统做出的决定并将其传达给非专家，知之甚少。*拟议研究计划的长期目标是更好地正式理解安全和可解释的机器学习的要求和方法。为了开始应对这一挑战，在接下来的几年里，我们计划探索交互式机器学习的潜力。到目前为止，交互式学习的研究大多集中在如何设计主动标签查询来高效地在有监督学习任务中实现高统计精度。在这里，我们旨在探索学习过程中主动反馈循环的潜力，以实现其他三个目标：*(1)安全-随着机器学习技术进入关键应用，如自动驾驶汽车或医疗设备，我们需要比目前依赖的统计保证更强的正确行为保证。*(2)适应性-机器学习系统通常在与其受雇环境不同的环境中进行训练。因此，我们需要让系统适应这种变化的方法。*(3)可解释性--由于机器学习系统被用于直接影响人类个人的决策支持，例如在金融部门或在司法系统中，我们需要开发方法，使这些决策能够被人类理解。*为此，我们计划开发与学习系统进行用户交互的新协议。从基础的角度开发这种技术将有助于在这项快速发展的技术和使用机器学习或受到机器学习影响的非专家之间架起桥梁。

The proposed research is in the area of developing theoretical foundations to machine learning. Machine learning technologies are being used in various every-day life systems with an ever growing influence on industry, science and society. This includes commercial usage such as in recommendation systems or personal devices, but also critical applications such as autonomous vehicles, brain-machine interfaces or medical assistance. As machine learning practice currently enjoys enormous success and learning methods are employed in a vast number of application domains, it becomes increasingly crucial to develop solid and formal analysis and understanding of these methods. Many of the currently most successful learning methods (in particular deep learning based ones) have a “blackbox” nature. While they are becoming enormously successful at building accurate predictors based on large amounts of data, there is a growing societal sense of discomfort about data-driven tools affecting people's lives in ever expanding, yet uncontrolled, ways. We have, in fact, distressingly little understanding on why and how these methods work, what influences their outcomes, what type of correctness guarantees one can provide for their outcomes, and how we may interpret and communicate decisions made by a machine learned system to non-experts.******The long-term goal of the proposed research program is to develop a better formal understanding of requirements and methods for safe and interpretable machine learning. To start addressing this challenge, over the next few years, we plan to explore the potential of interactive machine learning for this purpose. So far, research in interactive learning has mostly focused on how to design active label queries to efficiently achieve high statistical accuracy in supervised learning tasks. Here we aim to explore the potential of active feedback loops in learning process for three other objectives:******(1) Safety – as machine learning technologies enter critical applications, such as self-driving cars or medical devices, we need stronger guarantees of correct behavior than the statistical guarantees we currently rely on.***(2) Adaptability – machine learning systems are often trained in environments that are different from the one they are employed in. Thus, we need ways to let the system adapt to such changes.***(3) Interpretability – as machine learned systems are used for decision support directly affecting human individuals such as in the financial sector or in the judicial system, we need to develop ways to make these decisions comprehensible by humans. ******To this aim, we plan to develop novel protocols of user interaction with a learning system. Developing such from a foundational perspective will help building bridges between this fast developing technology and non-experts who are using or who are affected by machine learning.