Algorithmic stability in machine learning

algorithms for statistical problems. In particular, we are interested in understanding how techniques such as subsampling or iterative optimization procedures (e.g., noisy gradient descent or SGD) can be used during or after training in order to enhance stability of certain estimation algorithms; and what general algorithmic tools may be developed for transforming a procedure which is stable in one sense into a procedure which is stable in another sense. We will also be very interested in studying discrete-structured problems, such as graph-based estimation or ranking procedures, which have received far less attention in the literature than their continuous counterparts.

Student Desirable Background:

A strong mathematical background, preferably with undergraduate-level knowledge of statistics theory, optimization, and basic algorithms and complexity.

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How to apply:

Please apply under the University of Manchester application portal and select 'PhD in Artificial Intelligence' (OAA Application form)

When applying, you must include the project’s full name, your supervisor’s name, funding status, academic history, and two referees’ contact details. Applications with missing documents will not be processed. Late or incomplete submissions will not be considered, and the university is not responsible for missed deadlines.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A mandatory one- to two-page statement explaining your motivation for postgraduate research, why you chose Manchester, relevant academic or work experience, key research findings, and the techniques or skills you've gained. Applications will be put on hold if this is not provided.
• Contact details for two referees (Please ensure referee email addresses are official university or workplace accounts, as we may need to verify the references.)
• English Language Certificate (if applicable)

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. (Equality, diversity and inclusion | The University of Manchester)

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We are dedicated to supporting work-life balance and offer flexible working arrangements to accommodate individual needs. Our selection process is free from bias, and we are committed to ensuring fair and equal opportunities for all applicants.

We are dedicated to supporting work-life balance and offer flexible working arrangements to accommodate individual needs, including a part-time study option.

Join us and help grow the UK’s national capability in artificial intelligence, part of the UK’s modern industrial strategy. Everyone is welcome if you are passionate about uncertainty quantification, decision making with humans in the loop and decision making for machine learning systems and their applications in Science and Engineering, and looking to develop your research and innovation skills at the doctoral level to further your career in the AI sector.

Funding Notes

The UKRI AI CDT in Decision Making for Complex Systems is part of the TechFirst offering stipends of £30,000 for students eligible for home fee status. The start date is September 2027.

The Project will be based in University of Cambridge.

References

• Balle, Barthe, and Gaboardi (2018). "Privacy amplification by subsampling: Tight analyses via couplings and divergences." Advances in NeurIPS.
• Bassily, Smith, and Thakurta (2014). "Private empirical risk minimization: Efficient algorithms and tight error bounds." FOCS.
• Bousquet and Elisseeff (2002). "Stability and generalization." JMLR.
• Impagliazzo, Lei, Pitassi, and Sorrell (2022). "Reproducibility in learning." STOC.
• Prasad, Suggala, Balakrishnan, and Ravikumar (2020). "Robust estimation via robust gradient estimation." JRSSB.
• Soloff, Barber, and Willett (2024). "Bagging provides assumption-free stability." JMLR.