Learning theory and methods for novel types of distributional shifts.

AI_CDT_DecisionMaking

Details

Distribution shifts pose major challenges for developing reliable machine learning systems that should be robust to changing conditions, especially when unexpected changes happen. In this project, the goal is to develop methods for tackling novel types of distributional shifts by combining development of the necessary new theory with development of methods and applying them to real cases with collaborators. The project can be tailored to focus more on theory or method development, depending on the interests of the student.

A starting question is the feasibility of using PAC-Bayes bounds for novel types of distributional shifts. These kinds of bounds have been used for learning robust majority vote rules in some binary classification problems with distribution shift, where the PAC-Bayes bounds guided a learning algorithm to find the weights for the weighted majority vote. The natural next steps then are to obtain PAC-Bayes bounds for multi-class problems and to derive corresponding learning strategies, with particular focus on exploring the capacity of the resulting models to distinguish structural differences between source and target samples.

Another interesting question asks to characterise properties of the likelihood that might induce robustness to distributional shifts or other perturbations. To answer this question, the project will study relaxations of the likelihood via implicit modelling where the likelihood isn't specified as such. In the absence of an explicit likelihood, some form of knowledge about it might be gained via upper bounds on functions of the likelihood. Specifically, a conjecture to study in this project is whether such bounds can be derived from the PAC-Bayesian analysis.

The situation is more complicated when distributional changes are unexpected and so it is no longer possible to specify a target distribution. A viable solution might be to design learning strategies such that the resulting prediction models are robust against the worst possible distribution from a given class of distributions. This is the idea behind distributionally robust optimisation (DRO) methods. The project will study the feasibility of applying DRO for tackling problems of unexpected distribution shift, and options to relax the potentially overly strict worst-case bounds.

Applications: The project will have outstanding opportunities for demonstrating the developed new methods in applications with collaborators, from cancer research, engineering design, biological and drug and materials design, experimental design.

Deliverables of the project:

- Publications in top-tier statistics and machine learning venues.

- New learning methods which will be made publicly available via open access.

- Demonstrators and case study with collaborators in a real application, for instance in design problems in engineering biology or drug design, or in personalized medicine.

Desirable Student Background:

Strong background in Probabilistic ML and/or Statistical Machine Learning. Comfortable with code and computer experiments, e.g. prototyping, reproducing others' experiments and so on..

Before you apply

We strongly recommend that you contact the supervisor(s) for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project. For any questions please contact the UKRI AI Decisions CDT Team (aidecisionscdt@manchester.ac.uk).

How to apply:

Please apply through the below link for the PhD Artificial Intelligence CDT:

https://pgapplication.manchester.ac.uk/psc/apply/EMPLOYEE/SA/s/WEBLIB_ONL_ADM.CIBAA_LOGIN_BT.FieldFormula.IScript_Direct_Login?Key=UMANC1251000021489F

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

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 one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language Certificate (if applicable)

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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.