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Publications

Gram-Hansen, B. et al. (2019) “Hijacking Malaria Simulators with Probabilistic Programming”, ICML Workshop on AI for Social Good, 2018 [Preprint].
Eric, N. et al. (2019) “Hybrid models with deep and invertible features”, in Proceedings of Machine Learning Research. Proceedings of Machine Learning Research, pp. 4723–4732.
Lee, J. et al. (2019) “Set transformer: A framework for attention-based permutation-invariant neural networks”, in Proceedings of Machine Learning Research. PMLR, pp. 3744–3753.
Goliński, A., Wood, F. and Rainforth, T. (2019) “Amortized Monte Carlo integration”, in Proceedings of Machine Learning Research. Proceedings of Machine Learning Research, pp. 2309–2318.
Lyddon, S., Holmes, C. and Walker, S. (2019) “General Bayesian updating and the loss-likelihood bootstrap”, Biometrika, 106(2), pp. 465–478.
Gram-Hansen, B. et al. (2019) “Hijacking Malaria Simulators with Probabilistic Programming”, arXiv.
Gram-Hansen, B. et al. (2019) “Hijacking Malaria Simulators with Probabilistic Programming”, arXiv.
Lee, J., Miscouridou, X. and Caron, F. (2019) “A unified construction for series representations and finite approximations of completely random measures”, arXiv.
Lee, J. et al. (2019) “Set transformer: A framework for attention-based permutation-invariant neural networks”, in. Proceedings of Machine Learning Research, pp. 3744–3753.
Richards, D. and Rebeschini, P. (2019) “Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up”, arXiv.