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Publications

Ghalebikesabi, S. et al. (2022) “Mitigating Statistical Bias within Differentially Private Synthetic Data”, in Proceedings of Machine Learning Research, pp. 685–695.
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.
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.
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.
Ernst, M., Reinert, G. and Swan, Y. (2022) “On Papathanasiou’s covariance expansions”, Latin American Journal of Probability and Mathematical Statistics, 19(2), pp. 1827 – 1827.
Reichelt, T. et al. (2022) “Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently”, in Proceedings of Machine Learning Research, pp. 1676–1685.
Reichelt, T., Ong, L. and Rainforth, T. (2022) “Rethinking Variational Inference for Probabilistic Programs with Stochastic Support”, in Advances in Neural Information Processing Systems.
Barrett, B. et al. (2022) “Certifiably Robust Variational Autoencoders”, in Proceedings of Machine Learning Research, pp. 3663–3683.
Kossen, J. et al. (2022) “Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation”, in Advances in Neural Information Processing Systems.