Adaptive and Robust Methods in Statistical Machine Learning

在当今瞬息万变的世界中，统计机器学习模型已经成为许多应用程序的关键，从推荐系统到自动驾驶汽车。然而，两个基本挑战可能会影响其有效性：持续适应和数据稀缺。持续适应是指统计机器学习模型随着环境随时间变化而学习和进化的能力。例如，在金融市场中，模型必须迅速适应不断变化的市场条件。数据稀缺是指在特定领域缺乏足够的信息，这可能是由于隐私问题、数据不可用或与数据收集相关的高成本而引起的。该项目的主要重点是开发新的鲁棒统计机器学习方法，以确保在动态和/或数据稀缺环境中的可靠性。我们非常强调这些方法的健壮性，这意味着它们应该在面对不断变化的条件时保持其性能。该项目的主要目标是：开发新的统计机器学习方法，能够不断适应不断变化的数据分布，同时确保鲁棒性，并且不会影响动态环境中的性能。-研究相关任务和领域之间知识转移的新技术，强调知识转移的鲁棒性，以减轻数据稀缺的影响，并在数据有限的情况下实现更准确的学习。-研究这些技术的基础，旨在建立稳健的保证和理论性质，以确保我们的模型预测的可靠性和透明度。拟议的研究项目在各个领域具有广泛的相关性。例如，在医疗保健领域，它可以使诊断模型有效地发挥作用，即使医疗数据有限，无法提供更准确的诊断。同样，在自动驾驶汽车中，这些技术可以通过不断适应不断变化的路况来确保更安全、更可靠的驾驶。此外，在环境监测中，这些方法可以通过纳入可用的新数据来提高气候模型的准确性，使预测对决策更可靠。这些只是适应性和鲁棒性统计机器学习如何解决复杂挑战的几个例子。该项目属于EPSRC数学科学研究领域。

In today's rapidly changing world, statistical machine learning models have become crucial to manyapplications, ranging from recommendation systems to autonomous vehicles. However, two fundamentalchallenges can compromise their effectiveness: continual adaptation and data scarcity. Continual adaptationrefers to the ability of statistical machine learning models to learn and evolve as the environment changesover time. For instance, in the financial market, models must rapidly adapt to the changing marketconditions. Data scarcity refers to the lack of adequate information in specific domains, which can arise dueto privacy concerns, data unavailability, or the high costs associated with data collection.The primary focus of this project is on developing novel robust statistical machine learning methods thatensure reliability in dynamic and/or data-scarce environments. We place a strong emphasis on therobustness of these methods, which means they should maintain their performance in the face of changingconditions. The main objectives of this project are:- To develop new statistical machine learning methods that can adapt continually to evolving datadistributions, while ensuring robustness and without compromising performance in dynamic environments.- To investigate novel techniques for knowledge transfer between related tasks and domains, placingemphasis on the robustness of knowledge transfer to mitigate the impact of data scarcity and enable moreaccurate learning even when data is limited.- To study the foundations of these techniques, aiming to establish robust guarantees and theoreticalproperties that ensure the reliability and transparency of our model predictions.The proposed research project has broad relevance across various fields. For instance, in healthcare, it canenable diagnostic models to function effectively even when medical data is limited to provide more accuratediagnoses. Similarly, in autonomous vehicles, these techniques can ensure safer and more reliable driving byconstantly adapting to shifting road conditions. Furthermore, in environmental monitoring, these methodscan improve the accuracy of climate models by incorporating new data as it becomes available, makingpredictions more reliable for decision-making. These are just a few examples of how adaptive and robuststatistical machine learning can address complex challenges.This project falls within the EPSRC Mathematical Sciences research area.