Publications

Rudner, T. et al. (2021) “On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations”, in Advances in Neural Information Processing Systems, pp. 28376–28389.

Ghalebikesabi, S. et al. (2021) “On Locality of Local Explanation Models”, in Advances in Neural Information Processing Systems, pp. 18395–18407.

Foster, A. et al. (2021) “Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design”, in Proceedings of Machine Learning Research, pp. 3384–3395.

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.

Pardo-Diaz, J. et al. (2021) “Generating weighted and thresholded gene coexpression networks using signed distance correlation”, bioRxiv.

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.

Rudner, T. et al. (2021) “On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes”, in Proceedings of Machine Learning Research, pp. 9148–9156.

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.