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Machine learning
Projects
Counterfactual fairness
Making algorithm-led decisions fair by ensuring their outcomes are the same in the actual world and a 'counterfactual world' where an individual belongs to a different demographic
Co-designing algorithms and computer architecture
Designing hardware to suit the needs of data science algorithms, which will similarly be designed to suit the capabilities of the hardware
London air quality
Developing machine learning algorithms and data science platforms to understand and improve air quality over London
Capturing complex data streams
Describing complex sequences of data from different sources, to gain insights and generate meaningful actions in a range of applications
Cancer pre-diagnostic analytics with AI
Creating a digital repository of a variety of tumour and immune cells and developing algorithms to recognise these cells automatically
Bayesian analysis of single cell sequencing data
Developing statistical tools for biological data, to understand complex biological processes, such as cancer
Publications and software
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
Interpretability of representations in both deep generative and discriminative models is highly desirable. Current...
Tameem Adel, Zoubin Ghahramani, Adrian Weller; Proceedings of the 35th International Conference on Machine Learning, PMLR 80:50-59, 2018.
Blind Justice: Fairness with Encrypted Sensitive Attributes
Recent work has explored how to train machine learning models which do not discriminate...
Kilbertus, N., Gascon, A., Kusner, M., Veale, M., Gummadi, K.P. & Weller, A.. (2018). Blind Justice: Fairness with Encrypted Sensitive Attributes. Proceedings of the 35th International Conference on Machine Learning, in PMLR 80:2635-2644
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Machine learning methods are widely used for a variety of prediction problems. Prediction as...
Sanyal, A., Kusner, M.J., Gascón, A., & Kanade, V. (2018). TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. CoRR, abs/1806.03461.
Handling Incomplete Heterogeneous Data using VAEs
Variational autoencoders (VAEs), as well as other generative models, have been shown to be...
A. Nazábal, P. M. Olmos, Z. Ghahramani, I. Valera "Handling Incomplete Heterogeneous Data using VAEs", Arxiv preprint, July 2018.
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices
Discrimination via algorithmic decision making has received considerable attention. Prior work largely focuses on...
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, Muhammad Bilal Zafar KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018, London, United Kingdom, August 2018
Labelling as an unsupervised learning problem
Unravelling hidden patterns in datasets is a classical problem with many potential applications. In...
Lyons, Terry, and Imanol Perez Arribas. "Labelling as an unsupervised learning problem." arXiv preprint arXiv:1805.03911 (2018).
Sparse communication for distributed gradient descent
We make distributed stochastic gradient descent faster by exchanging sparse updates instead of dense...
Aji, A & Heafield, K 2017, Sparse Communication for Distributed Gradient Descent. in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017). Association for Computational Linguistics (ACL), pp. 440-445, EMNLP 2017: Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 7-11 September.
Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning
With widespread use of machine learning methods in numerous domains involving humans, several studies...
Grgic-Hlaca, N., Zafar, M.B., Gummadi, K.P., & Weller, A. (2018). Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning. AAAI.
People
Dr Stuart Middleton
Turing Fellow
Dr Stephane Hess
Turing Fellow
Professor Simon Dixon
Turing Fellow
Professor Shannon Vallor
Turing Fellow
Dr Selin Ahipasaoglu
Turing Fellow
Professor Samuel Albanie
Turing Fellow
Dr Riccardo Di Clemente
Turing Fellow
Dr Qingyuan Zhao
Turing Fellow