Publications (Computational Statistics and Machine Learning) | Department of Statistics

Publications

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.

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.

Boyles, F., Deane, C. and Morris, G. (2019) “Learning from the Ligand: Using Ligand-Based Features to Improve Binding Affinity Prediction.”

Boyles, F., Deane, C. and Morris, G. (2019) “Learning from the ligand: using ligand-based features to improve binding affinity prediction.”