Compass at NeurIPS 2022

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


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.


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.


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.


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.


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.

Congratulations to Compass student for paper accepted for NeurIPS 2022 Proceedings

Congratulations to Compass PhD student, Anthony Stephenson, who along with his supervisors, Robert Allison and Ed Pyzer-Knapp (IBM Research) has had their paper Provably Reliable Large-Scale Sampling from Gaussian Processes  accepted to be published at NeurIPS 2022.

Anthony mentions:

“Gaussian processes are a highly flexible class of non-parametric Bayesian models used in a variety of applications. In their exact form they provide principled uncertainty representations, at the expense of poor scalability (O(n^3)) with the number of training points. As a result, many approximate methods have been proposed to try and address this. We raise the question of how to assess the performance of such methods. The most obvious approach is to generate data from the exact GP model and then benchmark performance metrics of the approximations against the data generating process. Unfortunately, generating data from an exact GP is also in general an O(n^3) problem. We address this limitation by demonstrating how tunable parameters controlling the fidelity of inexact methods of drawing samples can be chosen to ensure that their samples are, with high probability, indistinguishable from genuine data from the exact GP.”

For more information: [2211.08036] Provably Reliable Large-Scale Sampling from Gaussian Processes (

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