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.

Compass at NeurIPS 2022

A post by Anthony Stephenson, Jack Simons, and Dan Ward, PhD students on the Compass programme.

Introduction

Ant Stephenson, Jack Simons, and I (Dan Ward) had the pleasure of attending the 2022 Conference on Neural Information Processing Systems (NeurIPS), one of the largest machine learning conferences in the world. The conference was held in New Orleans, which gave us an opportunity to explore a lively city full of culture with delicious local cuisine. We thought we’d collaborate on a blog post together covering some of the highlights.

Memorable Talks

The conference had broad range of talks including technical presentations of research, applied projects, and discussions of the philosophical and ethical questions that arise in AI. To give a taste of some of the talks, we picked out some of our favourites below.

Noam Brown: Human Modelling and Strategic Reasoning in the Game of Diplomacy.

The Game of Diplomacy is a strategic board game invented in 1954. It’s unique feature, and of crucial importance of the game, is that players interact via natural language to form allegiances. Whilst AI has been successful in beating humans in many purely adversarial games (e.g. Chess, Go), this collaborative element poses unique challenges. Firstly, it isn’t obvious how to evaluate/devise strategies for collaboration/betrayal, especially in the self-play-based reinforcement learning paradigm. Secondly, as communication happens via natural language, the AI must be able to translate their strategic plan into text. This strange combination of problems lead to interesting and innovative solutions. Paper link here.

Geoffrey Hinton: The Forward-Forward Algorithm for Training Deep Neural Networks.

Among the great line-up of speakers was Professor Geoffrey Hinton, known for popularising backpropogation for deep neural networks. Inspired by producing a more biologically plausible algorithm for learning, he has proposed the ‘Forward-Forward’ algorithm which he claims can also explain the phenomena of sleep! Professor Geoffrey Hinton then went on to express his belief that using biologically-inspired hardware, so-called neuromorphic computing, may play a key role in advancing AI. The talk was certainly unconventional, but nevertheless entertaining. Paper link here.

David Chalmers: Could a Large Language Model be Conscious?

Amongst all the machine-learning experts was David Chalmers, a philosopher! There are important questions regarding the possibility that language models might be conscious. David Chalmers aimed to educate the machine-learning audience in attendance of how we can better think about these problems and re-phrase the questions that we’re asking. We concluded that these questions are, unsurprisingly, best left to philosophers!

Poster Sessions


Jack and Dan:

I (Dan), presented a poster of my work at the conference, on Robust Neural Posterior Estimation (paper link here). I was definitely surprised by the scale of the poster sessions, and the broad scope of all the work taking place. Below is some of the posters that me and Jack found interesting:

Contrastive Neural Ratio Estimation
Benjamin K. Miller · Christoph Weniger · Patrick Forré
Authors propose NRE-C which aims to generalise NRE-A (Hermans et al. (2019)) and NRE-B (Durkan et al. (2020)) into one method. NRE-C can recover both methods by taking their two introduced hyperparameters at certain limits. Paper link here.

Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
Arnaud Delaunoy · Joeri Hermans · François Rozet · Antoine Wehenkel · Gilles Louppe
These authors also make a contribution to the field of neural ratio estimation in the simulation-based inference context. Authors propose the notion of a “balanced” classifier, which is a classifier in which the average output from the classifier over the positive data class plus the average output over the negative data class equals to 1. The authors argue that if one has a classifier is balanced then it will lead to more conservative posterior estimates, which is something which practitioners seek. To integrate this into an algorithm they suggest adding a penalisation term onto the standard logistic-loss which punishes classifiers as they become less balanced. Paper link here.

Training and Inference on Any-Order Autoregressive Models the Right Way
Andy Shih · Dorsa Sadigh · Stefano Ermon
A joint distribution can be decomposed into its univariate conditionals by the chain rule, although by doing so we implicitly choose an ordering in a model, which prevents arbitrary conditional inference. Any-order autoregressive models circumvent this generally by being trained such that all possible univariate conditionals are considered, but this leads to learning redundant information. The paper proposes a new method to train autoregressive models, using a subset of univariate conditionals that still supports arbitrary conditional inference. This research was also presented as a talk, but sadly we missed it!  Paper link here.

Anthony:

The poster sessions formed the bulk of the conference timetable, with 2 2-hour sessions per day, on Tuesday, Wednesday and Thursday. These were very busy, with many posters on a wide-range of topics and a large congregation of attendees. As a result, it was sometimes difficult to track down the subset of posters on material of particular interest and when this feat was achieved, on occasion it was still hard work to actually have a detailed conversation with the author(s). Nonetheless, it was interesting to see the how varied the subjects of the poster were and in addition get a feeling for “themes” of the conference: recurring, clearly in-vogue topics. Amongst the sea of posters, I did manage to find a number relating to GPs; of these, those I found most interesting were:

Posterior and Computational Uncertainty in Gaussian Processes:
Jonathan Wenger · Geoff Pleiss · Marvin Pförtner · Philipp Hennig · John Cunningham
Here the authors propose a way to naturally incorporate uncertainty introduced from the use of (iterative) GP approximation methods. Paper link here.

Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
Vidhi Lalchand · Wessel Bruinsma · David Burt · Carl Edward Rasmussen
The authors attempt to integrate a fully-Bayesian inference procedure for sparse GPs, as an alternative to the commonly adopted approach of optimising the kernel hyperparameters by maximum likelihood estimation. Paper link here.

Log-Linear-Time Gaussian Processes Using Binary Tree Kernels:
Michael K. Cohen · Samuel Daulton · Michael A Osborne
The idea here feels a bit unorthodox; they use a “binary-tree” kernel which discretises the space, with quantization error determined by the number of leaves. This would seem to lose interpretability on the properties of the function prior (e.g. smoothness), but does appear to give empirical benefits in their experiments. Paper link here.

Workshops

In addition to the main conference, on the Friday and Saturday at the end of the week there were a selection of workshops on a variety of sub-fields within machine learning. If you are fortunate enough for there to be a workshop dedicated to your research area, then they provide a space to gather people with research directly relevant to your own and facilitate helpful discussions and networking opportunities.

Anthony:

For me, the “Gaussian processes, spatiotemporal modeling and decision-making systems” workshop was the most useful part of the conference. It gave me the chance to speak to people working on interesting problems related to my own; discover the kind of directions they are heading in and lines of work they are contemplating. Additionally, I presented a poster during this workshop which allowed me to discuss my work with an audience well-versed on the topic and its possible significance.

The Big Easy

In addition to the actual conference, attending NeurIPS also gave us the opportunity to explore the city of New Orleans; aka The Big Easy. Upon arrival, we were immediately greeted in the airport by the sound of Louis Armstrong, a strong theme in the city, which features a park named after him. New ‘Awlins’ is well known for its jazz, but awareness of this fact does not necessarily prepare you for the sheer quantity, especially in the streets of the French Quarter, that awaits you. The real epicentre of jazz in the city is situated on Frenchmen street, on which a swathe of bars hosting nearly-nightly live music reside. We spent several evenings there, including one of particular note, where French president Emmanuel Macron suddenly appeared, trailed by an extensive retinue of blue-suited aides and bodyguards. Another street in New Orleans infamous for its nightlife is Bourbon street. Where Frenchmen street is focused on jazz, Bourbon street contains all manner of rowdy madness, assaulting your senses with noise, smells and sights as soon as you arrive. Both are necessary experiences when visiting The Big Easy.

Conclusion

All in all, the conference was a great opportunity to get a taste of the massive array of research that occurs in machine learning. We were all surprised by the scope of the research topics and talks, and enjoyed the opportunity to explore a new culture and city.

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)

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