Publications

Mathieu, E. et al. (2019) “Continuous hierarchical representations with poincaré Variational Auto-Encoder”, in Advances in Neural Information Processing Systems 32: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Curran Associates, pp. 12521–12532.

Ge, S. et al. (2019) “Random tessellation forests”, in Advances in Neural Information Processing Systems 32: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Curran Associates, pp. 9543–9553.

Foster, A. et al. (2019) “Variational Bayesian optimal experimental design”, in Advances in Neural Information Processing Systems 32 (NIPS 2019). Conference on Neural Information Processing Systems.

Rao, D. et al. (2019) “Continual unsupervised representation learning”, in Advances in Neural Information Processing Systems 32 (NIPS 2019). Conference on Neural Information Processing Systems.

Foster, A. et al. (2019) “Variational Bayesian Optimal Experimental Design”, in.

Martínez-Rubio, D., Kanade, V. and Rebeschini, P. (2019) “Decentralized cooperative stochastic bandits”, in Advances in Neural Information Processing Systems 32. Neural Information Processing Systems Foundation.

Richards, D. and Rebeschini, P. (2019) “Optimal statistical rates for decentralised non-parametric regression with linear speed-up”, in Advances in Neural Information Processing Systems 32 (NIPS 2019). Neural Information Processing Systems Foundation.

Foster, A. et al. (2019) “A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments.”

Fawsitt, C. et al. (2019) “Cost-effectiveness analysis of baseline testing for resistance-associated polymorphisms to optimize treatment outcome in genotype 1 noncirrhotic treatment-naïve patients with chronic hepatitis C virus”, Value in Health, 23(2), pp. 180–190.

Naik, C., Caron, F. and Rousseau, J. (2019) “Sparse Networks with Core-Periphery Structure”, arXiv.