Publications

Lehmann, B. et al. (2021) “Optimal strategies for learning multi-ancestry polygenic scores vary across traits”, p. 2021.01.15.426781.

Hutchinson, M. et al. (2021) “LieTransformer: Equivariant Self-Attention for Lie Groups”, in Proceedings of Machine Learning Research, pp. 4533–4543.

Holderrieth, P., Hutchinson, M. and Teh, Y. (2021) “Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes”, in Proceedings of Machine Learning Research, pp. 4297–4307.

Mathieu, E., Foster, A. and Teh, Y. (2021) “On Contrastive Representations of Stochastic Processes”, in Advances in Neural Information Processing Systems, pp. 28823–28835.

Xu, W. and Reinert, G. (2021) “A Stein Goodness-of-fit Test for Exponential Random Graph Models”, in Proceedings of Machine Learning Research, pp. 415–423.

Rudner, T. et al. (2021) “On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes”, in Proceedings of Machine Learning Research, pp. 9148–9156.

Wang, B., Webb, S. and Rainforth, T. (2021) “Statistically Robust Neural Network Classification”, in Proceedings of Machine Learning Research, pp. 1735–1745.

Foster, A. et al. (2021) “Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design”, in Proceedings of Machine Learning Research, pp. 3384–3395.

Xu, J. et al. (2021) “Group Equivariant Subsampling”, in Advances in Neural Information Processing Systems, pp. 5934–5946.

Ivanova, D. et al. (2021) “Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods”, in Advances in Neural Information Processing Systems, pp. 25785–25798.