Bayesian Neural Learning via Langevin Dynamics for Chaotic Time Series Prediction. - Rohitash Chandra; Lamiae Azizi; Sally Cripps (ICONIP (5), 2017)

Bayesian Multi-task Learning for Dynamic Time Series Prediction. - Rohitash Chandra; Sally Cripps (IJCNN, 2018)

Multi-core parallel tempering Bayeslands for basin and landscape evolution. - Rohitash Chandra; R. Dietmar Müller; Ratneel Deo; Nathaniel Butterworth; Tristan Salles; Sally Cripps (CoRR, 2018)

BayesReef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics. - Jodie Pall; Rohitash Chandra; Danial Azam; Tristan Salles; Jody M. Webster; Sally Cripps (CoRR, 2018)

Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success. - Richard Scalzo; David Kohn; Hugo K. H. Olierook; Gregory Houseman; Rohitash Chandra; Mark A. Girolami; Sally Cripps (CoRR, 2018)

Coevolutionary multi-task learning for feature-based modular pattern classification. - Rohitash Chandra; Sally Cripps (Neurocomputing, 2018)

Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression. - Román Marchant; Sebastian Haan; Garner Clancey; Sally Cripps (Secur. Informatics, 2018)

Structured Variational Inference in Continuous Cox Process Models. - Virginia Aglietti; Edwin V. Bonilla; Theodoros Damoulas; Sally Cripps (NeurIPS, 2019)

Bayesian Nonparametric Adaptive Spectral Density Estimation for Financial Time Series. - Nick James; Román Marchant; Richard Gerlach; Sally Cripps (CoRR, 2019)

Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands. - Rohitash Chandra; Danial Azam; R. Dietmar Müller; Tristan Salles; Sally Cripps (Comput. Geosci., 2019)

Langevin-gradient parallel tempering for Bayesian neural learning. - Rohitash Chandra; Konark Jain; Ratneel Vikash Deo; Sally Cripps (Neurocomputing, 2019)

Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics. - Jodie Pall; Rohitash Chandra; Danial Azam; Tristan Salles; Jody M. Webster; Richard Scalzo; Sally Cripps (Environ. Model. Softw., 2020)

Non-Volume Preserving Hamiltonian Monte Carlo and No-U-TurnSamplers. - Hadi Mohasel Afshar; Rafael Oliveira 001; Sally Cripps (AISTATS, 2021)

Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning. - Rohitash Chandra; Sally Cripps; Nathaniel Butterworth; R. Dietmar Müller (Environ. Model. Softw., 2021)

Fixed-Distance Hamiltonian Monte Carlo. - Hadi Mohasel Afshar; Sally Cripps (NeurIPS, 2022)

Remaining useful life prediction: A multiple product partition approach. - John W. Lau; Edward Cripps; Sally Cripps (Commun. Stat. Simul. Comput., 2022)

AdaptSPEC-X: Covariate-Dependent Spectral Modeling of Multiple Nonstationary Time Series. - Michael Bertolacci; Ori Rosen; Edward Cripps; Sally Cripps (J. Comput. Graph. Stat., 2022)

Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning. - Yun Li; Zhe Liu 023; Saurav Jha; Sally Cripps; Lina Yao 001 (CoRR, 2023)

Optimal Particle-based Approximation of Discrete Distributions (OPAD). - Hadi Mohasel Afshar; Gilad Francis; Sally Cripps (CoRR, 2024)

Covariate Dependent Mixture of Bayesian Networks. - Román Marchant; Dario Draca; Gilad Francis; Sahand Assadzadeh; Mathew Varidel; Frank Iorfino; Sally Cripps (CoRR, 2025)

Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning-Based Recommendation. - Siyu Wang 001; Xiaocong Chen; Julian J. McAuley; Sally Cripps; Lina Yao 001 (IEEE Trans. Neural Networks Learn. Syst., 2025)