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University of Oxford, Department of Statistics logo
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Publications

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.
Wang, B., Webb, S. and Rainforth, T. (2021) “Statistically Robust Neural Network Classification”, in 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021, 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.
Farquhar, S., Gal, Y. and Rainforth, T. (2021) “ON STATISTICAL BIAS IN ACTIVE LEARNING: HOW AND WHEN TO FIX IT”, in Iclr 2021 9th International Conference on Learning Representations.
Foster, A., Pukdee, R. and Rainforth, T. (2021) “IMPROVING TRANSFORMATION INVARIANCE IN CONTRASTIVE REPRESENTATION LEARNING”, in Iclr 2021 9th International Conference on Learning Representations.
Di Benedetto, G., Caron, F. and Teh, Y. (2020) “Non-exchangeable random partition models for microclustering”. University of Oxford.
Di Benedetto, G., Caron, F. and Teh, Y. (2020) “Non-exchangeable random partition models for microclustering”. University of Oxford.
Willetts, M., Roberts, S. and Holmes, C. (2020) “Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels”, in. Institute of Electrical and Electronics Engineers (IEEE), pp. 5286–5295.
Wu, F. and Rebeschini, P. (2020) “A continuous-time mirror descent approach to sparse phase retrieval”, in Advances in Neural Information Processing Systems 33 (NeurIPS 2020). Neural Information Processing Systems Foundation, Inc., pp. 1–12.
Vaškevičius, T., Kanade, V. and Rebeschini, P. (2020) “The statistical complexity of early-stopped mirror descent”, in. Neural Information Processing Systems Foundation, Inc., pp. 1–12.