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University of Oxford, Department of Statistics logo
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Publications

Zaidi, S. et al. (2021) “Neural Ensemble Search for Uncertainty Estimation and Dataset Shift”, in Advances in Neural Information Processing Systems, pp. 7898–7911.
Goto, A. et al. (2021) “Understanding the Genetics of Viral Drug Resistance by Integrating Clinical Data and Mining of the Scientific Literature”, bioRxiv.
Zhu, X. et al. (2021) “Identification of Underlying Disease Domains by Longitudinal Latent Factor Analysis for Secukinumab Treated Patients in Psoriatic Arthritis and Rheumatoid Arthritis Trials”, in ARTHRITIS & RHEUMATOLOGY, pp. 2516–2518.
Naik, C., Caron, F. and Rousseau, J. (2021) “Sparse networks with core-periphery structure”, Electronic Journal of Statistics, 15(1), pp. 1814–1868.
Kossen, J. et al. (2021) “Active Testing: Sample-Efficient Model Evaluation”, in Proceedings of Machine Learning Research, pp. 5753–5763.
Wang, B., Webb, S. and Rainforth, T. (2021) “Statistically Robust Neural Network Classification”, in Proceedings of Machine Learning Research, pp. 1735–1745.
Kossen, J. et al. (2021) “Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning”, in Advances in Neural Information Processing Systems, pp. 28742–28756.
Tolpin, D. et al. (2021) “Probabilistic Programs with Stochastic Conditioning”, in Proceedings of Machine Learning Research, pp. 10312–10323.
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.
Foster, A., Pukdee, R. and Rainforth, T. (2021) “IMPROVING TRANSFORMATION INVARIANCE IN CONTRASTIVE REPRESENTATION LEARNING”, in Iclr 2021 9th International Conference on Learning Representations.