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Publications

De Bortoli, V. et al. (2023) “Riemannian score-based generative modelling”, in Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Curran Associates, pp. 2406–2422.
Xu, W. and Reinert, G. (2023) “A kernelised Stein statistic for assessing implicit generative models”, Advances in Neural Information Processing Systems 35.
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.
Holmes, C. and Walker, S. (2023) “Statistical inference with exchangeability and martingales”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381(2247), pp. 20220143 – 20220143.
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 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.
Johnson, E., Pike-Burke, C. and Rebeschini, P. (2023) “Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes”, arXiv.