Compass students at AISTATS 2023

Congratulations to Compass students Josh Givens, Hannah Sansford and Alex Modell who, along with their supervisors have had their papers accepted to be published at AISTATS 2023.

 

‘Implications of sparsity and high triangle density for graph representation learning’

Hannah Sansford, Alexander Modell, Nick Whiteley, Patrick Rubin-Delanchy

Hannah: In this paper we explore the implications of two common characteristics of real-world networks, sparsity and triangle-density, for graph representation learning. An example of where these properties arise in the real-world is in social networks, where, although the number of connections each individual has compared to the size of the network is small (sparsity), often a friend of a friend is also a friend (triangle-density). Our result counters a recent influential paper that shows the impossibility of simultaneously recovering these properties with finite-dimensional representations of the nodes, when the probability of connection is modelled by the inner-product. We, by contrast, show that it is possible to recover these properties using an infinite-dimensional inner-product model, where representations lie on a low-dimensional manifold. One of the implications of this work is that we can ‘zoom-in’ to local neighbourhoods of the network, where a lower-dimensional representation is possible.

The paper has been selected for oral presentation at the conference in Valencia (<2% of submissions). 

 

Density Ratio Estimation and Neyman Pearson Classification with Missing Data

Josh Givens, Song Liu, Henry W J Reeve

Josh: In our paper we adapt the popular density ratio estimation procedure KLIEP to make it robust to missing not at random (MNAR) data and demonstrate its efficacy in Neyman-Pearson (NP) classification. Density ratio estimation (DRE) aims to characterise the difference between two classes of data by estimating the ratio between their probability densities. The density ratio is a fundamental quantity in statistics appearing in many settings such as classification, GANs, and covariate shift making its estimation a valuable goal. To our knowledge there is no prior research into DRE with MNAR data, a missing data paradigm where the likelihood of an observation being missing depends on its underlying value. We propose the estimator M-KLIEP and provide finite sample bounds on its accuracy which we show to be minimax optimal for MNAR data. To demonstrate the utility of this estimator we apply it the the field of NP classification. In NP classification we aim to create a classifier which strictly controls the probability of incorrectly classifying points from one class. This is useful in any setting where misclassification for one class is much worse than the other such as fault detection on a production line where you would want to strictly control the probability of classifying a faulty item as non-faulty. In addition to showing the efficacy of our new estimator in this setting we also provide an adaptation to NP classification which allows it to still control this misclassification probability even when fit using MNAR data.

Applications now open for PhD in Computational Statistics and Data Science

Start your PhD in Data Science now

Compass CDT is now recruiting for its fully funded places to start September 2023.

We are happy to announce that The University of Bristol online application system is open, and we are receiving applications for Compass CDT programme for September 2023 start. Early application is advised.

For 2023/34 entry, applicants must review the projects on offer. The projects are listed in the research section of our website. You will need to provide a Research Statement in your application documents with a ranked list of 3 projects of interest to you: 1 being the project of highest interest.

PhD Project Allocation Process

Application forms will be reviewed based on the 3 ranked projects specified. Successful applicants will be invited to attend an interview with the Compass admissions tutors and the specific project supervisor. If you are made an offer of PhD study it will be published through the online application system. You will then have 2 weeks to consider the offer before deciding whether to accept or decline.

The next review of applications for 2023 funded places will take place after

4 January 2023.

APPLY NOW

We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ and black, Asian and minority ethnic communities, to join us.

Advantages of being a Compass Student

  • Stipend – a generous stipend of £21,668 pa tax free, paid in monthly payments. Plus your own expense budget of £1,000 pa towards travel and research activity.
  • No fees – all tuition fees are covered by the EPSRC and University of Bristol.
  • Bespoke training – first year units are designed specifically for the academic needs of each Compass student, which enables students to develop knowledge and capability to pursue cross-disciplinary PhD research.
  • Supervisors – supervisors from across academic disciplines offer a range of research projects.
  • Cohort – Compass students benefit from dedicated offices and collaboration spaces, enabling strong cohort links and opportunities for shared learning and research.

About Compass CDT

A 4-year bespoke PhD training programme in the statistical and computational techniques of data science, with partners from across the University of Bristol, industry and government agencies.

The cross-disciplinary programme offers exciting collaborations across medicine, computer science, geography, economics, life and earth sciences, as well as with our external partners who range from government organisations such as the Office for National Statistics, NCSC and the AWE, to industrial partners such as LV, Improbable, IBM Research, EDF, and AstraZeneca.

Students are co-located with the Institute for Statistical Science in the School of Mathematics, which occupies the Fry Building.

Hear from our students about their experience with the programme

  • Compass has allowed me to advance my statistical knowledge and apply it to a range of exciting applied projects, as well as develop skills that I’m confident will be highly useful for a future career in data science. – Shannon, Cohort 2

  • With the Compass CDT I feel part of a friendly, interactive environment that is preparing me for whatever I move on to next, whether it be in Academia or Industry. – Sam, Cohort 2

  • An incredible opportunity to learn the ever-expanding field of data science, statistics and machine learning amongst amazing people. – Danny, Cohort 1

APPLY BEFORE: 

Wednesday 4 January 2023, 5pm (London, UK time zone)

APPLY NOW

Video: The Data Science behind COVID Modelling

We are excited to share Dr Daniel Lawson’s (Compass CDT Co-Director) latest video where he will tell you about the Data Science behind Bristol’s COVID Modelling.

Mathematics has had a hidden role in predicting how we can best fight COVID-19. How is mathematics used with data science and machine learning? Why is modelling epidemics such a hard problem? How can we do it better next time? What will data science be able to do in the future, and how do you become a part of it?

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