Sequential Neural Methods for Likelihood-free Inference. - Conor Durkan; George Papamakarios; Iain Murray 001 (CoRR, 2018)

Autoregressive Energy Machines. - Conor Durkan; Charlie Nash (ICML, 2019)

Neural Spline Flows. - Conor Durkan; Artur Bekasov; Iain Murray 001; George Papamakarios (NeurIPS, 2019)

Cubic-Spline Flows. - Conor Durkan; Artur Bekasov; Iain Murray 001; George Papamakarios (CoRR, 2019)

On Contrastive Learning for Likelihood-free Inference. - Conor Durkan; Iain Murray 001; George Papamakarios (ICML, 2020)

sbi: A toolkit for simulation-based inference. - Álvaro Tejero-Cantero; Jan Boelts; Michael Deistler; Jan-Matthis Lueckmann; Conor Durkan; Pedro J. Gonçalves; David S. Greenberg; Jakob H. Macke (J. Open Source Softw., 2020)

Maximum Likelihood Training of Score-Based Diffusion Models. - Yang Song 011; Conor Durkan; Iain Murray 001; Stefano Ermon (NeurIPS, 2021)

On Maximum Likelihood Training of Score-Based Generative Models. - Conor Durkan; Yang Song 011 (CoRR, 2021)

Continuous diffusion for categorical data. - Sander Dieleman; Laurent Sartran; Arman Roshannai; Nikolay Savinov; Yaroslav Ganin; Pierre H. Richemond; Arnaud Doucet; Robin Strudel; Chris Dyer; Conor Durkan; Curtis Hawthorne; Rémi Leblond; et al. (CoRR, 2022)

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. - Yilun Du; Conor Durkan; Robin Strudel; Joshua B. Tenenbaum; Sander Dieleman; Rob Fergus; Jascha Sohl-Dickstein; Arnaud Doucet; Will Sussman Grathwohl (ICML, 2023)

sbi reloaded: a toolkit for simulation-based inference workflows. - Jan Boelts; Michael Deistler; Manuel Glöckler; Álvaro Tejero-Cantero; Jan-Matthis Lueckmann; Guy Moss 001; Peter Steinbach; Thomas Moreau 001; Fabio Muratore; Julia Linhart; Conor Durkan; Julius Vetter; et al. (J. Open Source Softw., 2025)