Publications

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.

Xu, W. and Reinert, G. (2022) “AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators”, in Advances in Neural Information Processing Systems.

He, Y. et al. (2022) “MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian”, in Proceedings of Machine Learning Research.

Barrett, B. et al. (2022) “Certifiably Robust Variational Autoencoders”, in Proceedings of Machine Learning Research, pp. 3663–3683.

Xu, W. and Reinert, G. (2022) “A Kernelised Stein Statistic for Assessing Implicit Generative Models”, in Advances in Neural Information Processing Systems.

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.

Miao, N. et al. (2022) “ON INCORPORATING INDUCTIVE BIASES INTO VAES”, in ICLR 2022 - 10th International Conference on Learning Representations.

Kossen, J. et al. (2022) “Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation”, in Advances in Neural Information Processing Systems.