#### Congratulations to Compass students Hannah Sansford and Alex Modell who, along with their supervisors Patrick Rubin-Delanchy and Nick Whiteley, have had their paper ‘Implications of sparsity and high triangle density for graph representation learning’ accepted to be published at AISTATS 2023, as well as being selected for oral presentation at the conference in Valencia (<2% of submissions).

Congratulations also to Compass student Josh Givens who, alongside supervisors Song Liu and Henry Reeve, have had their paper “Density Ratio Estimation and Neyman Pearson Classification with Missing Data” accepted to be published at AISATS 2023.

The breadth of Hannah and Alex’s work is as follows:

“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.”

Read here: https://arxiv.org/abs/2210.15277

Josh’s research also encompasses the following:

“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 a new estimator M-KLIEP to tackle this problem as well as providing a new method to perform NP classification in the presence of MNAR data using this new estimator.”

Read here: https://arxiv.org/abs/2302.10655

*The 26th International Conference on Artificial Intelligence and Statistics (AISTATS) will be held on April 25 – April 27, 2023. The conference will be held at Palau de Congressos, Valencia, Spain as an in-person event.*