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Publications

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.
Fong, E. and Holmes, C. (2021) “Conformal Bayesian Computation”, in Advances in Neural Information Processing Systems, pp. 18268–18279.
Rudner, T. et al. (2021) “On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations”, in Advances in Neural Information Processing Systems, pp. 28376–28389.
Wilde, H. et al. (2021) “Foundations of Bayesian Learning from Synthetic Data”, in Proceedings of Machine Learning Research, pp. 541–549.
Schwarz, J. et al. (2021) “Powerpropagation: A sparsity inducing weight reparameterisation”, in Advances in Neural Information Processing Systems, pp. 28889–28903.
Hutchinson, M. et al. (2021) “Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independant Projected Kernels”, in Advances in Neural Information Processing Systems, pp. 17160–17169.
Chau, S. et al. (2021) “BAYESIMP: Uncertainty Quantification for Causal Data Fusion”, in Advances in Neural Information Processing Systems, pp. 3466–3477.