Delegation of computation for machine learning

This research falls into the areas of machine learning and quantum computing. Machine learning is ubiquitous in our daily lives, from Spotify recommendations to estimating how long your commute is going to take today. Delegating machine learning tasks to a more powerful third-party in the cloud (for example) offers the potential to make more difficult learning problems tractable, which is a very attractive prospect. At present, however, we blindly trust that the third-party in the cloud is doing a good job. This is fine for less sensitive tasks such as song recommendations, but is much less desirable in more sensitive situations. This research will aim to produce protocols that remove this need for blind trust,
and instead allow us to verify that the the third-party has indeed done a sufficiently good job.
We will aim to develop a theory of how to delegate machine learning, i.e. asking a powerful computer in the cloud to carry out a machine learning task for you. This will include the development of protocols which allow machine learning tasks to be delegated in a way which doesn't require trust, allowing us to verify that what the powerful computer in the cloud is telling us is in fact true. We will aim to produce protocols for this delegation which are transparent, privacy-preserving and post-quantum secure.
This work is highly interdisciplinary, comprising the areas of cryptographic proofs and machine learning. This combination is a new direction of research. We will use deep technical tools from the theory of cryptographic proofs to approach the problem of delegating machine learning via novel methodologies.
Delegating machine learning tasks to a more powerful computer is an attractive prospect. However, ideally, we would prefer not to have to trust that said powerful computer is doing a good job, and would rather have some way of verifying this fact for ourselves. The aim of this work is to produce protocols which remove this need for trust, and facilitate the delegation of machine learning tasks in a verifiable, transparent, privacy-preserving way. These protocols could immediately be applied to any delegated machine learning task in which it is important that the output is sufficiently high quality. Both sides stand to gain from this, as the side doing the verifying has a guarantee that what they are receiving is up to scratch, while the side doing the learning will now have the means to convince anyone that they are doing a good job.
This work falls within the areas of machine learning and quantum information. It looks at these two areas with respect to the area of delegation of learning, which are priority areas for EPSRC, and it strives to make a significant industrial and societal impact.

External Partner; StarkWare

In the 2018 Government Office for Science report, 'Computational Modelling: Technological Futures', Greg Clarke, the Secretary of State for Business Energy and Industrial Strategy, wrote "Computational modelling is essential to our future productivity and competitiveness, for businesses of all sizes and across all sectors of the economy". With its focus on computational models, the mathematics that underpin them, and their integration with complex data, the MathSys II CDT will generate diverse impacts beyond academia. This includes impacts on skills, on the economy, on policy and on society.

Impacts on skills.
MathSys II will produce a minimum of 50 PhD graduates to support the growing national demand for advanced mathematical modelling and data analysis skills. The CDT will provide each of them with broad core skills in the MSc, a deep knowledge of their chosen research specialisation in the PhD and a complementary qualification in transferable skills integrated throughout. Graduates will thus acquire the profiles needed to form the next generation of leaders in business, government and academia. They will be supported by an integrated pastoral support framework, including a diverse group of accessible leadership role models. The cohort based environment of the CDT provides a multiplier effect by encouraging cohorts to forge long-lasting professional networks whose value and influence will long outlast the CDT itself. MathSys II will seek to maximise the influence of these networks by providing topical training in Responsible Research and Innovation, by maintaining a robust Equality, Diversity & Inclusion policy, and by integration with Warwick's global network of international partnerships.

Economic impacts.
The research outputs from many MathSys II PhD projects will be of direct economic value to commercial, public sector and charitable external partners. Engagement with CDT partners will facilitate these impacts. This includes co-supervision of PhD and MSc projects, co-creation of Research Study Groups, and a strong commitment to provide placements/internships for CDT students. When commercial innovations or IP are generated, we will work with Warwick Ventures, the commercial arm of the University of Warwick, to commercialise/license IP where appropriate. Economic impact may also come from the creation of new companies by CDT graduates. MathSys II will present entrepreneurship as a viable career option to students. One external partner, Spectra Analytics, was founded by graduates of the preceding Complexity Science CDT, thus providing accessible role models. We will also provide in-house entrepreneurship training via Warwick Ventures and host events by external start-up accelerator Entrepreneur First.

Impacts on policy.
The CDT will influence policy at the national and international level by working with external partners operating in policy. UK examples include Department of Health, Public Health England and DEFRA. International examples include World Health Organisation (WHO) and the European Commission for the Control of Foot-and-mouth Disease (EuFMD). MathSys students will also utilise the recently announced UKRI policy internships scheme.

Impacts on society.
Public engagement will allow CDT students to promote the value of their research to society at large. Aside from social media, suitable local events include DataBeers, Cafe Scientifique, and the Big Bang Fair. MathSys will also promote a socially-oriented ethos of technology for the common good. Concretely, this includes the creation of open-source software, integration of software and data carpentry into our computational and data driven research training and championing open-access to research. We will also contribute to the 'innovation culture and science' strand of Coventry's 2021 City of Culture programme.