Adaptive and Robust Methods in Statistical Machine Learning

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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.