Publications

Ghalebikesabi, S. et al. (2022) “Mitigating Statistical Bias within Differentially Private Synthetic Data”, in Proceedings of Machine Learning Research, pp. 685–695.

Rudner, T. et al. (2022) “Continual Learning via Sequential Function-Space Variational Inference”, in Proceedings of Machine Learning Research, pp. 18871–18887.

Ghalebikesabi, S. et al. (2022) “Mitigating Statistical Bias within Differentially Private Synthetic Data”, in Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022, pp. 696–705.

Venkatesh, S. et al. (2022) “Genetic architecture of longitudinal obesity trajectories in primary care electronic health records”, in HUMAN HEREDITY, pp. 21–22.

Clivio, O. et al. (2022) “Neural Score Matching for High-Dimensional Causal Inference”, in Proceedings of Machine Learning Research, pp. 7076–7110.

Nicholson, G. et al. (2022) “Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework”, Nature Microbiology, 7(1), pp. 97–107.

Wu, F. and Rebeschini, P. (2021) “Implicit regularization in matrix sensing via mirror descent”, in Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Neural Information Processing Systems Foundation.

Farghly, T. and Rebeschini, P. (2021) “Time-independent generalization bounds for SGLD in non-convex settings”, in Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Neural Information Processing Systems Foundation, pp. 19836–19846.

Richards, D., Negahban, S. and Rebeschini, P. (2021) “Distributed machine learning with sparse heterogeneous data”, in Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Neural Information Processing Systems Foundation, pp. 18008–18020.