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Publications

He, Y. et al. (2022) “PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs”, arXiv.
He, Y. et al. (2022) “GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks.”
Bennett, S., Cucuringu, M. and Reinert, G. (2022) “Lead-lag detection and network clustering for multivariate time series with an application to the US equity market.”
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.
He, Y., Reinert, G. and Cucuringu, M. (2022) “DIGRAC: Digraph Clustering Based on Flow Imbalance”, in Proceedings of Machine Learning Research.
Clivio, O. et al. (2022) “Neural Score Matching for High-Dimensional Causal Inference”, in Proceedings of Machine Learning Research, pp. 7076–7110.
Rudner, T. et al. (2022) “Tractable Function-Space Variational Inference in Bayesian Neural Networks”, in Advances in Neural Information Processing Systems.
Rudner, T. et al. (2022) “Continual Learning via Sequential Function-Space Variational Inference”, in Proceedings of Machine Learning Research, pp. 18871–18887.
Xu, W. and Reinert, G. (2022) “A Kernelised Stein Statistic for Assessing Implicit Generative Models”, in Advances in Neural Information Processing Systems.
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.