Publications

Xu, W. and Reinert, G. (2023) “AgraSSt: approximate graph Stein statistics for interpretable assessment of implicit graph generators”, Advances in Neural Information Processing Systems 35.

Campbell, A. et al. (2023) “A continuous time framework for discrete denoising models”, in Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Curran Associates, pp. 28266–28279.

Naik, C. et al. (2023) “Bayesian Nonparametrics for Sparse Dynamic Networks”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Nature, pp. 191–206.

Naik, C. et al. (2023) “Bayesian Nonparametrics for Sparse Dynamic Networks”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Nature, pp. 191–206.

Naik, C. et al. (2023) “Bayesian nonparametrics for sparse dynamic networks”, in Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Springer Nature, pp. 191–206.

Naik, C. et al. (2023) “Bayesian nonparametrics for sparse dynamic networks”, in Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Springer Nature, pp. 191–206.

Johnson, E., Pike-Burke, C. and Rebeschini, P. (2023) “Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes”, arXiv.

Lu, Y., Reinert, G. and Cucuringu, M. (2023) “Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets”, arXiv.

Lu, Y., Reinert, G. and Cucuringu, M. (2023) “Co-Trading Networks for Modeling Dynamic Interdependency Structures and Estimating High-Dimensional Covariances in US Equity Markets”, SSRN Electronic Journal.

Caron, F. et al. (2023) “Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning”, arXiv.