Continual Metalearning

Context of the research and potential impact
Meta learning, also referred to as "learning to learn", studies techniques and machine learning models able to adapt and generalise to new tasks and environments quickly, without having to go through a long training process every time. Continual learning focuses on the development of methods that can deal with a continuous stream of data.
Advancements in continual meta-learning would hugely influence the application of modern machine learning models in industry, economy and society, where data is often received in a continuous stream. For machine learning models to be deployed in the real-world, they need to be able to detect model/data mismatch, concept drift, anomalies and out of sample quantification. The models require metrics to quantify when, how and to what extent performance is degrading, to determine when their parameters need to be updated.
Our goal is to create models that can distinguish between meaningful and not meaningful changes in the data, and adapt to the first. Adaptive models need a dynamical architecture and they are appropriate when working with sequential streams of data. Detecting and quickly adapting to meaningful changes is critical in the real-world, for models' predictions to stay reliable.
Aims and Objectives
The aim of this project is to advance the field of continual meta-learning, to enable a meaningful integration of machine learning models in industry settings and to deal with real-world data.

The first task of the project is to provide an overlook of existing techniques. In particular, we will focus on Neural Processes and on extending them to the continual learning domain. Neural Processes are hybrid models, that like Gaussian Processes define distributions over functions, and have computationally efficient training like Neural Networks, with the ability to adapt their priors to data [Garnelo et al., 2018b,a]. Subsequent research can be done on the inclusion of meaningful inductive biases for and from multiple domains, and on the transfer learning abilities of ensembles/reservoirs of Neural Processes.
During the DPhil we will explore the development of dynamical models with the ability of detecting model-data mismatch, concept drift, anomalies and out of sample quantification. An approach would be to recognise which past training information are now obsolete and remove them, with the model updating its parameters accordingly.
A good knowledge of existing performance metrics is needed, to understand what may still be missing and eventually develop other metrics useful to update the model and compensate for anomalous and drifting data.
A long-term goal of this research is to make continual meta-learning models applicable on real-world data, such as time series data or spatio temporal data to tackle environmental problems.
Novelty of the research methodology
Neural Processes are a relatively new architecture, initially proposed by Garnelo et al. [2018b,a]. Jha et al. [2022] presents a summary of the most recent applications of Neural Processes. To our knowledge, the application of Neural Processes to continual learning settings, or using an ensemble of Neural Pro-cesses for transfer learning, have not been yet explored. Moreover, as their development is fairly recent, work needs to be done to assess their applicability in real-world settings.
As new dynamical and adaptive models are developed, it is likely that we will need additional metrics to assess their features and overall performance.
Alignment to EPSRC's strategies and research areas
The research aligns with the EPSRC research area of Artificial Intelligence technologies, as the research is carried out in a machine learning group in partnership with the spin-out Mind Foundry, that applies cutting edge machine learning to deliver solutions to business and industry.

AIMS's impact will be felt across domains of acute need within the UK. We expect AIMS to benefit: UK economic performance, through start-up creation; existing UK firms, both through research and addressing skills needs; UK health, by contributing to cancer research, and quality of life, through the delivery of autonomous vehicles; UK public understanding of and policy related to the transformational societal change engendered by autonomous systems.

Autonomous systems are acknowledged by essentially all stakeholders as important to the future UK economy. PwC claim that there is a £232 billion opportunity offered by AI to the UK economy by 2030 (10% of GDP). AIMS has an excellent track record of leadership in spinout creation, and will continue to foster the commercial projects of its students, through the provision of training in IP, licensing and entrepreneurship. With the help of Oxford Science Innovation (investment fund) and Oxford University Innovation (technology transfer office), student projects will be evaluated for commercial potential.

AIMS will also concretely contribute to UK economic competitiveness by meeting the UK's needs for experts in autonomous systems. To meet this need, AIMS will train cohorts with advanced skills that span the breadth of AI, machine learning, robotics, verification and sensor systems. The relevance of the training to the needs of industry will be ensured by the industrial partnerships at the heart of AIMS. These partnerships will also ensure that AIMS will produce research that directly targets UK industrial needs. Our partners span a wide range of UK sectors, including energy, transport, infrastructure, factory automation, finance, health, space and other extreme environments.

The autonomous systems that AIMS will enable also offer the prospect of epochal change in the UK's quality of life and health. As put by former Digital Secretary Matt Hancock, "whether it's improving travel, making banking easier or helping people live longer, AI is already revolutionising our economy and our society." AIMS will help to realise this potential through its delivery of trained experts and targeted research. In particular, two of the four Grand Challenge missions in the UK Industrial Strategy highlight the positive societal impact underpinned by autonomous systems. The "Artificial Intelligence and data" challenge has as its mission to "Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030". To this mission, AIMS will contribute the outputs of its research pillar on cancer research. The "Future of mobility" challenge highlights the importance the autonomous vehicles will have in making transport "safer, cleaner and better connected." To this challenge, AIMS offers the world-leading research of its robotic systems research pillar.

AIMS will further promote the positive realisation of autonomous technologies through direct influence on policy. The world-leading academics amongst AIMS's supervisory pool are well-connected to policy formation e.g. Prof Osborne serving as a Commissioner on the Independent Commission on the Future of Work. Further, Dr Dan Mawson, Head of the Economy Unit; Economy and Strategic Analysis Team at BEIS will serve as an advisor to AIMS, ensuring bidirectional influence between policy objectives and AIMS research and training.

Broad understanding of autonomous systems is crucial in making a society robust to the transformations they will engender. AIMS will foster such understanding through its provision of opportunities for AIMS students to directly engage with the public. Given the broad societal importance of getting autonomous systems right, AIMS will deliver core training on the ethical, governance, economic and societal implications of autonomous systems.