Nonparametric regression using linear combinations of basis functions. - Robert Kohn; Michael S. Smith; David Chan (Stat. Comput., 2001)

Representation and Self-Similarity of Shapes. - Davi Geiger; Tyng-Luh Liu; Robert Kohn (IEEE Trans. Pattern Anal. Mach. Intell., 2003)

Estimation and variable selection in nonparametric heteroscedastic regression. - Paul Yau; Robert Kohn (Stat. Comput., 2003)

A general approach to heteroscedastic linear regression. - David S. Leslie; Robert Kohn; David J. Nott (Stat. Comput., 2007)

Evaluation of Modeling, Simulation and Optimization Approaches for Work Flow Management in Semiconductor Manufacturing. - Robert Kohn; Daniel Noack; Marcin Mosinski; Zhugen Zhou; Oliver Rose (WSC, 2009)

Bayesian covariance matrix estimation using a mixture of decomposable graphical models. - Helen Armstrong; Christopher K. Carter; Kin Foon Kevin Wong; Robert Kohn (Stat. Comput., 2009)

Bayesian variable selection and model averaging in the arbitrage pricing theory model. - Rachida Ouysse; Robert Kohn (Comput. Stat. Data Anal., 2010)

Automated generation of analytical process time models for cluster tools in semiconductor manufacturing. - Robert Kohn; Oliver Rose (WSC, 2011)

Constructing priors based on model size for nondecomposable Gaussian graphical models: A simulation based approach. - Christopher K. Carter; Frederick Wong; Robert Kohn (J. Multivar. Anal., 2011)

Study on optimization potential influencing factors in simulation studies focused on parallel batch machine scheduling using variable neighbourhood search. - Robert Kohn; Oliver Rose (WSC, 2012)

Study on multi-objective optimization for parallel batch machine scheduling using variable neighbourhood search. - Robert Kohn; Oliver Rose; Christoph Laroque (WSC, 2013)

A Framework for Batch Scheduling with Variable Neighborhood Search in Wafer Fabrication. - Robert Kohn (2015)

Adaptive Metropolis-Hastings sampling using reversible dependent mixture proposals. - Minh-Ngoc Tran; Michael K. Pitt; Robert Kohn (Stat. Comput., 2016)

On some variance reduction properties of the reparameterization trick. - Ming Xu 015; Matias Quiroz; Robert Kohn; Scott A. Sisson (CoRR, 2018)

Variance reduction properties of the reparameterization trick. - Ming Xu 015; Matias Quiroz; Robert Kohn; Scott A. Sisson (AISTATS, 2019)

Hamiltonian Monte Carlo with Energy Conserving Subsampling. - Khue-Dung Dang; Matias Quiroz; Robert Kohn; Minh-Ngoc Tran; Mattias Villani (J. Mach. Learn. Res., 2019)

Computationally efficient Bayesian estimation of high-dimensional Archimedean copulas with discrete and mixed margins. - David Gunawan; Minh-Ngoc Tran; Kosuke Suzuki; Josef Dick; Robert Kohn (Stat. Comput., 2019)

Spectral Subsampling MCMC for Stationary Time Series. - Robert Salomone; Matias Quiroz; Robert Kohn; Mattias Villani; Minh-Ngoc Tran (ICML, 2020)

Subsampling sequential Monte Carlo for static Bayesian models. - David Gunawan; Khue-Dung Dang; Matias Quiroz; Robert Kohn; Minh-Ngoc Tran (Stat. Comput., 2020)

A flexible particle Markov chain Monte Carlo method. - Eduardo F. Mendes; Christopher K. Carter; David Gunawan; Robert Kohn (Stat. Comput., 2020)

The Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC. - Matias Quiroz; Minh-Ngoc Tran; Mattias Villani; Robert Kohn; Khue-Dung Dang (J. Comput. Graph. Stat., 2021)

The Contextual Lasso: Sparse Linear Models via Deep Neural Networks. - Ryan Thompson; Amir Dezfouli; Robert Kohn (NeurIPS, 2023)

Realized recurrent conditional heteroskedasticity model for volatility modelling. - Chen Liu; Chao Wang; Minh-Ngoc Tran; Robert Kohn (CoRR, 2023)

Particle Mean Field Variational Bayes. - Minh-Ngoc Tran; Paco Tseng; Robert Kohn (CoRR, 2023)

DeepVol: A Deep Transfer Learning Approach for Universal Asset Volatility Modeling. - Chen Liu; Minh-Ngoc Tran; Chao Wang; Richard Gerlach; Robert Kohn (CoRR, 2023)

Dynamic Mixture of Experts Models for Online Prediction. - Parfait Munezero; Mattias Villani; Robert Kohn (Technometrics, 2023)

Automatically adapting the number of state particles in SMC2. - Imke Botha; Robert Kohn; Leah F. South; Christopher C. Drovandi (Stat. Comput., 2023)

Contextual Directed Acyclic Graphs. - Ryan Thompson; Edwin V. Bonilla; Robert Kohn (AISTATS, 2024)

ProDAG: Projection-induced variational inference for directed acyclic graphs. - Ryan Thompson; Edwin V. Bonilla; Robert Kohn (CoRR, 2024)

Flexible Variational Bayes Based on a Copula of a Mixture. - David Gunawan; Robert Kohn; David J. Nott (J. Comput. Graph. Stat., 2024)

Structured Variational Approximations with Skew Normal Decomposable Graphical Models and Implicit Copulas. - Robert Salomone; Xuejun Yu 002; David J. Nott; Robert Kohn (J. Comput. Graph. Stat., 2024)

Variational Bayesian inference for models with nuisance parameters and an intractable likelihood. - Y. H. Paco Tseng; Minh-Ngoc Tran; Robert Kohn (Stat. Comput., 2025)