David J. Nott - RankMe

48 papers Journal 44Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood. J. Comput. Graph. Stat. Nhat-Minh Nguyen, Minh-Ngoc Tran, Christopher C. Drovandi, David J. Nott
2025	J	`jnl`	Bayesian Regularized Regression Copula Processes for Multivariate Responses. J. Comput. Graph. Stat. Nadja Klein, Michael Stanley Smith, Ryan A. Chisholm, David J. Nott
2025	J	`jnl`	Bayesian Score Calibration for Approximate Models. J. Mach. Learn. Res. Joshua J. Bon, David J. Warne, David J. Nott, Christopher C. Drovandi
2025	J	`jnl`	Multi-objective Bayesian optimization for Likelihood-Free inference in sequential sampling models of decision making. Trans. Mach. Learn. Res. David Chen, Xinwei Li, Eui-Jin Kim, Prateek Bansal, David J. Nott
2025	J	`jnl`	Positional Encoder Graph Quantile Neural Networks for Geographic Data. Trans. Mach. Learn. Res. William E. R. de Amorim, Scott A. Sisson, Thais Carvalho Valadares Rodrigues, David J. Nott, Guilherme S. Rodrigues
2025	J	`jnl`	Simulation-based Bayesian inference under model misspecification. CoRR Ryan P. Kelly, David J. Warne, David T. Frazier, David J. Nott, Michael U. Gutmann, Christopher C. Drovandi
2024	—	`conf`	Dropout Regularization in Extended Generalized Linear Models Based on Double Exponential Families. ECML/PKDD (6) Benedikt Lütke Schwienhorst, Lucas Kock, Nadja Klein, David J. Nott
2024	J	`jnl`	Flexible Variational Bayes Based on a Copula of a Mixture. J. Comput. Graph. Stat. David Gunawan, Robert Kohn, David J. Nott
2024	J	`jnl`	Improving the Accuracy of Marginal Approximations in Likelihood-Free Inference via Localization. J. Comput. Graph. Stat. Christopher C. Drovandi, David J. Nott, David T. Frazier
2024	J	`jnl`	Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference. Trans. Mach. Learn. Res. Ryan P. Kelly, David J. Nott, David T. Frazier, David J. Warne, Christopher C. Drovandi
2024	J	`jnl`	Model-Free Local Recalibration of Neural Networks. CoRR R. Torres, David J. Nott, Scott A. Sisson, T. Rodrigues, J. G. Reis, G. S. Rodrigues
2024	J	`jnl`	Positional Encoder Graph Quantile Neural Networks for Geographic Data. CoRR William E. R. de Amorim, Scott A. Sisson, T. Rodrigues, David J. Nott, Guilherme S. Rodrigues
2024	J	`jnl`	Structured Variational Approximations with Skew Normal Decomposable Graphical Models and Implicit Copulas. J. Comput. Graph. Stat. Robert Salomone, Xuejun Yu, David J. Nott, Robert Kohn
2023	J	`jnl`	Correction to : Variational inference and sparsity in high-dimensional deep Gaussian mixture models. Stat. Comput. Lucas Kock, Nadja Klein, David J. Nott
2023	J	`jnl`	Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families. CoRR Benedikt Lütke Schwienhorst, Lucas Kock, David J. Nott, Nadja Klein
2023	J	`jnl`	Misspecification-robust Sequential Neural Likelihood. CoRR Ryan P. Kelly, David J. Nott, David T. Frazier, David J. Warne, Chris Drovandi
2023	J	`jnl`	Modularized Bayesian analyses and cutting feedback in likelihood-free inference. Stat. Comput. Atlanta Chakraborty, David J. Nott, Christopher C. Drovandi, David T. Frazier, Scott A. Sisson
2022	J	`jnl`	Variational inference and sparsity in high-dimensional deep Gaussian mixture models. Stat. Comput. Lucas Kock, Nadja Klein, David J. Nott
2021	J	`jnl`	A Semiautomatic Method for History Matching Using Sequential Monte Carlo. SIAM/ASA J. Uncertain. Quantification Christopher C. Drovandi, David J. Nott, Daniel Edward Pagendam
2021	J	`jnl`	Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance. J. Comput. Graph. Stat. Xuejun Yu, David J. Nott, Minh-Ngoc Tran, Nadja Klein
2021	J	`jnl`	Detecting conflicting summary statistics in likelihood-free inference. Stat. Comput. Yinan Mao, Xueou Wang, David J. Nott, Michael Evans
2021	J	`jnl`	Marginally Calibrated Deep Distributional Regression. J. Comput. Graph. Stat. Nadja Klein, David J. Nott, Michael Stanley Smith
2020	J	`jnl`	Conditionally structured variational Gaussian approximation with importance weights. Stat. Comput. Linda S. L. Tan, Aishwarya Bhaskaran, David J. Nott
2020	J	`jnl`	Likelihood-free approximate Gibbs sampling. Stat. Comput. G. S. Rodrigues, David J. Nott, Scott A. Sisson
2020	J	`jnl`	Robust Bayesian synthetic likelihood via a semi-parametric approach. Stat. Comput. Ziwen An, David J. Nott, Christopher C. Drovandi
2018	J	`jnl`	Gaussian variational approximation with sparse precision matrices. Stat. Comput. Linda S. L. Tan, David J. Nott
2018	J	`jnl`	Likelihood-free inference in high dimensions with synthetic likelihood. Comput. Stat. Data Anal. Victor M. H. Ong, David J. Nott, Minh-Ngoc Tran, Scott A. Sisson, Christopher C. Drovandi
2018	J	`jnl`	Using History Matching for Prior Choice. Technometrics Xueou Wang, David J. Nott, Christopher C. Drovandi, Kerrie L. Mengersen, Michael Evans
2018	J	`jnl`	Variational Bayes with synthetic likelihood. Stat. Comput. Victor M. H. Ong, David J. Nott, Minh-Ngoc Tran, Scott A. Sisson, Christopher C. Drovandi
2017	J	`jnl`	Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model. Comput. Stat. Data Anal. Jialiang Li, David J. Nott, Y. Fan, Scott A. Sisson
2016	J	`jnl`	Functional regression approximate Bayesian computation for Gaussian process density estimation. Comput. Stat. Data Anal. G. S. Rodrigues, David J. Nott, Scott A. Sisson
2016	J	`jnl`	Variational inference for sparse spectrum Gaussian process regression. Stat. Comput. Linda S. L. Tan, Victor M. H. Ong, David J. Nott, Ajay Jasra
2014	J	`jnl`	Mixtures of experts for understanding model discrepancy in dynamic computer models. Comput. Stat. Data Anal. David J. Nott, Lucy A. Marshall, Mark Fielding, Shie-Yui Liong
2012	J	`jnl`	The ensemble Kalman filter is an ABC algorithm. Stat. Comput. David J. Nott, Lucy A. Marshall, Minh-Ngoc Tran
2012	J	`jnl`	The predictive Lasso. Stat. Comput. Minh-Ngoc Tran, David J. Nott, Chenlei Leng
2012	J	`jnl`	Variational approximation for heteroscedastic linear models and matching pursuit algorithms. Stat. Comput. David J. Nott, Minh-Ngoc Tran, Chenlei Leng
2011	J	`jnl`	Efficient MCMC Schemes for Computationally Expensive Posterior Distributions. Technometrics Mark Fielding, David J. Nott, Shie-Yui Liong
2010	J	`jnl`	A sign based loss approach to model selection in nonparametric regression. Stat. Comput. David J. Nott, Jialiang Li
2010	J	`jnl`	Bayesian projection approaches to variable selection in generalized linear models. Comput. Stat. Data Anal. David J. Nott, Chenlei Leng
2008	J	`jnl`	Predictive performance of Dirichlet process shrinkage methods in linear regression. Comput. Stat. Data Anal. David J. Nott
2007	J	`jnl`	A general approach to heteroscedastic linear regression. Stat. Comput. David S. Leslie, Robert Kohn, David J. Nott
2006	—	`conf`	Deflection Routing on a Torus Is Monotone. POSTA Ana Busic, Jean-Michel Fourneau, David J. Nott
2006	J	`jnl`	Semiparametric estimation of mean and variance functions for non-Gaussian data. Comput. Stat. David J. Nott
2005	—	`conf`	Convergence Routing under Bursty Traffic: Instability and an AIMD Controller. PASM@FM Jean-Michel Fourneau, David J. Nott
2005	J	`jnl`	Efficient sampling schemes for Bayesian MARS models with many predictors. Stat. Comput. David J. Nott, Anthony Y. C. Kuk, Hiep Duc
2004	—	`conf`	Mixed Routing for ROMEO Optical Burst. ISCIS Jean-Michel Fourneau, David J. Nott
2000	J	`jnl`	Multi-phase image modelling with excursion sets. Signal Process. David J. Nott, Richard J. Wilson
1997	J	`jnl`	Parameter estimation for excursion set texture models. Signal Process. David J. Nott, Richard J. Wilson