By Professor Scott Anthony Sisson

Conference Papers

Fan X; Li B; Li Y; Sisson SA, 2021, 'Poisson-Randomised DirBN: Large mutation is needed in Dirichlet belief networks', in Proceedings of Machine Learning Research, pp. 3068 - 3077

Fan X; Li B; Sisson SA, 2020, 'Online Binary Space Partitioning Forests', in Chiappa S; Calandra R (ed.), INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 108, ADDISON-WESLEY PUBL CO, ELECTR NETWORK, pp. 527 - 536, presented at 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), ELECTR NETWORK, 26 August 2020 - 28 August 2020, http://proceedings.mlr.press/v108/fan20a/fan20a.pdf

Li Y; Fan X; Chen L; Li B; Yu Z; Sisson SA, 2020, 'Recurrent dirichlet belief networks for interpretable dynamic relational data modelling', in Ijcai International Joint Conference on Artificial Intelligence, pp. 2470 - 2476

Fan X; Li B; Li C; Sisson S; Chen L, 2019, 'Scalable deep generative relational model with high-order node dependence', in Wallach H; Larochelle H; Beygelzimer A; d'Alche-Buc F; Fox E; Garnett R (eds.), NeurIPS 2019, 33rd Conference on Neural INformation Processing Systems 2019, NEURAL INFORMATION PROCESSING SYSTEMS (NIPS), Vancouver, Canada, presented at NeurIPS 2019, Vancouver, Canada, 08 December 2019 - 14 December 2019

Fan X; Li B; Sisson S, 2019, 'Binary space partitioning forests', in Chaudhuri K; Sugiyama M (ed.), AISTATS 2019, Proceedings of Machine Learning Research, MICROTOME PUBLISHING, Naha, Okinawa, Japan, presented at AISTATS 2019, Naha, Okinawa, Japan, 16 April 2019 - 18 April 2019, http://proceedings.mlr.press/v89/fan19b/fan19b.pdf

Fan X; Li B; Sisson SA, 2019, 'Binary Space Partitioning Forests', in Proceedings of Machine Learning Research, pp. 3022 - 3031

Xu M; Quiroz M; Kohn R; Sisson SA, 2019, 'Variance reduction properties of the reparameterization trick', in Aistats 2019 22nd International Conference on Artificial Intelligence and Statistics

Fan X; Li B; Sisson S, 2018, 'Rectangular bounding process', in Bengio S; Wallach H; Larochelle H; Grauman K; CesaBianchi N; Garnett R (eds.), NeurIPS 2018, 32nd Conference on Neural Information Processing Systems, NEURAL INFORMATION PROCESSING SYSTEMS (NIPS), Montreal, pp. 7620 - 7630, presented at NeurIPS 2018, 32nd Conference on Neural Information Processing Systems, Montreal, 03 December 2018 - 08 December 2018

Fan X; Li B; Sisson S, 2018, 'The binary space partitioning tree process', in Storkey A; PerezCruz F (ed.), AISTATS 2018, Proceedings of Machine Learning Research, PMLR, Playa Blanca, Lanzarote, Canary Islands, pp. 1859 - 1867, presented at 21st International Conference on Artificial Intelligence and Statistics : AISTATS 2018, Playa Blanca, Lanzarote, Canary Islands, 09 April 2018 - 11 April 2018, http://proceedings.mlr.press/v84/fan18b/fan18b.pdf

Fan X; Li B; Sisson SA, 2018, 'The Binary Space Partitioning-Tree Process', in Proceedings of Machine Learning Research

Zheng F; Westra S; Sisson S; Leonard M, 2014, 'Flood risk estimation in Australia's coastal zone: Modelling the dependence between extreme rainfall and storm surge', in Hydrology and Water Resources Symposium 2014 Hwrs 2014 Conference Proceedings, pp. 390 - 396

Conference Posters

Man N; Chrzanowska A; McKetin R; Price O; Gibbs D; Bruno R; Sisson S; Dietze P; Salom C; Degenhardt L; Peacock A, 2020, 'Trends in methamphetamine availability, use, and harms in Australia', presented at The 2020 NDARC Annual Research Symposium, 05 November 2020 - 26 November 2020, https://ndarc.med.unsw.edu.au/resource/trends-methamphetamine-harms-australia

Reports

Man N; Sisson S; McKetin R; Chrzanowska A; Bruno R; Dietze P; Price O; Degenhardt L; Gibbs D; Salom C; Peacock A, 2022, Trends in methamphetamine use, markets and harms in Australia, 2003-2019, NDARC, Sydney, http://dx.doi.org/10.26190/ad59-k695

Bennett Moses L; Land L; Zalnieriute M; Zhao S; Nicholson K; Sisson S; Tani Bertuol M; Zlatanova S; Brown G; Krebbs S, 2021, Submission: Australian Data Strategy Discussion Paper, http://dx.doi.org/10.26190/unsworks/28191, https://www.allenshub.unsw.edu.au/sites/default/files/inline-files/20210806%20HUB%20AUSCL%20UDASH%20CSI%20DIIU%20CSRI%20submission%20on%20Australian%20Data%20Strategy_0.pdf

Preprints

Yang Y; Quiroz M; Beranger B; Kohn R; Sisson SA, 2026, Analysing symbolic data by pseudo-marginal methods, http://dx.doi.org/10.48550/arxiv.2408.04419

Kock L; Sisson SA; Rodrigues GS; Nott DJ, 2026, Predictive variational inference for flexible regression models, http://dx.doi.org/10.48550/arxiv.2602.22582

Warne DJ; Zhu X; Steele TP; Johnston ST; Sisson SA; Faria M; Murphy RJ; Browning AP, 2025, A multilevel hierarchical framework for quantification of experimental heterogeneity, http://dx.doi.org/10.64898/2025.12.21.695338

Yang Y; Quiroz M; Kohn R; Sisson SA, 2025, A correlated pseudo-marginal approach to doubly intractable problems, http://dx.doi.org/10.48550/arxiv.2210.02734

Adams MP; Monsalve-Bravo GM; Dowdell LG; Sisson SA; Drovandi C, 2025, Efficient sampling from a multivariate normal distribution subject to linear equality and inequality constraints, http://dx.doi.org/10.48550/arxiv.2508.15292

Zhong P; Sisson SA; Beranger B, 2025, Fast and flexible inference for spatial extremes, http://dx.doi.org/10.48550/arxiv.2407.13958

de Amorim WER; Sisson SA; Rodrigues T; Nott DJ; Rodrigues GS, 2025, Positional Encoder Graph Quantile Neural Networks for Geographic Data, http://dx.doi.org/10.48550/arxiv.2409.18865

Balnozan I; Fiebig DG; Asher A; Kohn R; Sisson SA, 2025, Hidden Group Time Profiles: Heterogeneous Drawdown Behaviours in Retirement, http://dx.doi.org/10.48550/arxiv.2009.01505

Kock L; Rodrigues GS; Sisson SA; Klein N; Nott DJ, 2024, Calibrated Multivariate Regression with Localized PIT Mappings, http://dx.doi.org/10.48550/arxiv.2409.10855

Torres R; Nott DJ; Sisson SA; Rodrigues T; Reis JG; Rodrigues GS, 2024, Model-Free Local Recalibration of Neural Networks, http://dx.doi.org/10.48550/arxiv.2403.05756

Lyu Z; Sisson SA; Welsh AH, 2024, Increasing dimension asymptotics for two-way crossed mixed effect models, https://doi.org/10.1214/24-AOS2469

Lopatnikova A; Tran M-N; Sisson SA, 2022, An Introduction to Quantum Computing for Statisticians and Data Scientists, http://dx.doi.org/10.48550/arxiv.2112.06587

Chakraborty A; Nott DJ; Drovandi C; Frazier DT; Sisson SA, 2022, Modularized Bayesian analyses and cutting feedback in likelihood-free inference, http://dx.doi.org/10.48550/arxiv.2203.09782

Chen WY; Peters GW; Gerlach RH; Sisson SA, 2021, Dynamic Quantile Function Models, http://dx.doi.org/10.48550/arxiv.1707.02587

Chin V; Beavan A; Fransen J; Mayer J; Kohn R; Ryan LM; Sisson SA, 2021, Modelling age-related changes in executive functions of soccer players, http://dx.doi.org/10.48550/arxiv.2105.01226

Warne DJ; Sisson SA; Drovandi C, 2020, Vector operations for accelerating expensive Bayesian computations -- a tutorial guide, http://dx.doi.org/10.48550/arxiv.1902.09046

Beranger B; Padoan SA; Sisson SA, 2020, Estimation and uncertainty quantification for extreme quantile regions, http://dx.doi.org/10.48550/arxiv.1904.08251

Rahman PA; Beranger B; Roughan M; Sisson SA, 2020, Likelihood-based inference for modelling packet transit from thinned flow summaries, http://dx.doi.org/10.48550/arxiv.2008.13424

Whitaker T; Beranger B; Sisson SA, 2020, Logistic regression models for aggregated data, http://dx.doi.org/10.48550/arxiv.1912.03805

Plein M; O'Brien K; Holden M; Adams M; Baker C; Bean N; Sisson S; Bode M; Mengersen K; McDonald-Madden E, 2020, Stressor equivalents: A framework to prevent perverse outcomes in data-poor systems, http://dx.doi.org/10.22541/au.159283264.49749008

Beranger B; Stephenson AG; Sisson SA, 2020, High-dimensional inference using the extremal skew-$t$ process, http://dx.doi.org/10.48550/arxiv.1907.10187

Beranger B; Lin H; Sisson SA, 2020, New models for symbolic data analysis, http://dx.doi.org/10.48550/arxiv.1809.03659

Whitaker T; Beranger B; Sisson SA, 2020, Composite likelihood methods for histogram-valued random variables, http://dx.doi.org/10.48550/arxiv.1908.11548

Priddle JW; Sisson SA; Frazier DT; Drovandi C, 2020, Efficient Bayesian synthetic likelihood with whitening transformations, http://dx.doi.org/10.48550/arxiv.1909.04857

Chin V; Lee JYL; Ryan LM; Kohn R; Sisson SA, 2019, Multiclass classification of growth curves using random change points and heterogeneous random effects, http://dx.doi.org/10.48550/arxiv.1909.07550

Chin V; Gunawan D; Fiebig DG; Kohn R; Sisson SA, 2019, Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives, http://dx.doi.org/10.48550/arxiv.1806.07274

Rodrigues GS; Nott DJ; Sisson SA, 2019, Likelihood-free approximate Gibbs sampling, http://dx.doi.org/10.48550/arxiv.1906.04347

Zhang X; Beranger B; Sisson SA, 2019, Constructing Likelihood Functions for Interval-valued Random Variables, http://dx.doi.org/10.48550/arxiv.1608.00107

Xu M; Quiroz M; Kohn R; Sisson SA, 2018, Variance reduction properties of the reparameterization trick, http://dx.doi.org/10.48550/arxiv.1809.10330

Beranger B; Padoan SA; Xu Y; Sisson SA, 2018, Extremal properties of the multivariate extended skew-normal distribution, http://dx.doi.org/10.48550/arxiv.1810.00680

Beranger B; Padoan SA; Xu Y; Sisson SA, 2018, Extremal properties of the univariate extended skew-normal distribution, http://dx.doi.org/10.48550/arxiv.1805.03316

Nott DJ; Ong VM-H; Fan Y; Sisson SA, 2018, High-dimensional ABC, http://dx.doi.org/10.48550/arxiv.1802.09725

Beranger B; Duong T; Perkins-Kirkpatrick SE; Sisson SA, 2017, Exploratory data analysis for moderate extreme values using non-parametric kernel methods, http://dx.doi.org/10.48550/arxiv.1602.08807

Lin H; Caley MJ; Sisson SA, 2017, Estimating global species richness using symbolic data meta-analysis, http://dx.doi.org/10.48550/arxiv.1711.03202

Rodrigues GS; Prangle D; Sisson SA, 2017, Recalibration: A post-processing method for approximate Bayesian computation, http://dx.doi.org/10.48550/arxiv.1704.06374

Rodrigues GS; Francis AR; Sisson SA; Tanaka MM, 2017, Inferences on the acquisition of multidrug resistance in \emph{Mycobacterium tuberculosis} using molecular epidemiological data, http://dx.doi.org/10.48550/arxiv.1704.04355

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