Capacity estimation of two-dimensional channels using Sequential Monte Carlo. - Christian A. Naesseth; Fredrik Lindsten; Thomas B. Schön (ITW, 2014)

Sequential Monte Carlo for Graphical Models. - Christian A. Naesseth; Fredrik Lindsten; Thomas B. Schön (NIPS, 2014)

Nested Sequential Monte Carlo Methods. - Christian A. Naesseth; Fredrik Lindsten; Thomas B. Schön (ICML, 2015)

Interacting Particle Markov Chain Monte Carlo. - Tom Rainforth; Christian A. Naesseth; Fredrik Lindsten; Brooks Paige; Jan-Willem van de Meent; Arnaud Doucet; Frank D. Wood (ICML, 2016)

Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms. - Christian A. Naesseth; Francisco J. R. Ruiz; Scott W. Linderman; David M. Blei (AISTATS, 2017)

Variational Sequential Monte Carlo. - Christian A. Naesseth; Scott W. Linderman; Rajesh Ranganath; David M. Blei (AISTATS, 2018)

Machine learning using approximate inference: Variational and sequential Monte Carlo methods. - Christian Andersson Naesseth (2018)

Elements of Sequential Monte Carlo. - Christian A. Naesseth; Fredrik Lindsten; Thomas B. Schön (Found. Trends Mach. Learn., 2019)

High-Dimensional Filtering Using Nested Sequential Monte Carlo. - Christian A. Naesseth; Fredrik Lindsten; Thomas B. Schön (IEEE Trans. Signal Process., 2019)

Markovian Score Climbing: Variational Inference with KL(p||q). - Christian A. Naesseth; Fredrik Lindsten; David M. Blei (NeurIPS, 2020)

variational combinatorial sequential monte carlo methods for bayesian phylogenetic inference. - Antonio Khalil Moretti; Liyi Zhang; Christian A. Naesseth; Hadiah Venner; David M. Blei; Itsik Pe'er (UAI, 2021)

Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. - Luhuan Wu; Brian L. Trippe; Christian A. Naesseth; David M. Blei; John P. Cunningham (NeurIPS, 2023)

Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport. - Liyi Zhang; David M. Blei; Christian A. Naesseth (Trans. Mach. Learn. Res., 2023)

A Variational Perspective on Generative Flow Networks. - Heiko Zimmermann; Fredrik Lindsten; Jan-Willem van de Meent; Christian A. Naesseth (Trans. Mach. Learn. Res., 2023)

Neural Diffusion Models. - Grigory Bartosh; Dmitry P. Vetrov; Christian A. Naesseth (ICML, 2024)

Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling. - Grigory Bartosh; Dmitry P. Vetrov; Christian Andersson Naesseth (NeurIPS, 2024)

Equivariant Neural Diffusion for Molecule Generation. - François Cornet; Grigory Bartosh; Mikkel N. Schmidt; Christian Andersson Naesseth (NeurIPS, 2024)

Variational Flow Matching for Graph Generation. - Floor Eijkelboom; Grigory Bartosh; Christian Andersson Naesseth; Max Welling; Jan-Willem van de Meent (NeurIPS, 2024)

Fast yet Safe: Early-Exiting with Risk Control. - Metod Jazbec; Alexander Timans; Tin Hadzi Veljkovic; Kaspar Sakmann; Dan Zhang; Christian Andersson Naesseth; Eric T. Nalisnick (NeurIPS, 2024)

VISA: Variational Inference with Sequential Sample-Average Approximations. - Heiko Zimmermann; Christian Andersson Naesseth; Jan-Willem van de Meent (NeurIPS, 2024)

E-Valuating Classifier Two-Sample Tests. - Teodora Pandeva; Tim Bakker; Christian A. Naesseth; Patrick Forré (Trans. Mach. Learn. Res., 2024)

Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics. - Hanming Yang; Antonio Khalil Moretti; Sebastian Macaluso; Philippe Chlenski; Christian A. Naesseth; Itsik Pe'er (Trans. Mach. Learn. Res., 2024)

Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space. - Alex Chen; Philippe Chlenski; Kenneth Munyuza; Antonio Khalil Moretti; Christian A. Naesseth; Itsik Pe'er (AISTATS, 2025)

Max-Rank: Efficient Multiple Testing for Conformal Prediction. - Alexander Timans; Christoph-Nikolas Straehle; Kaspar Sakmann; Christian A. Naesseth; Eric T. Nalisnick (AISTATS, 2025)

On Continuous Monitoring of Risk Violations under Unknown Shift. - Alexander Timans; Rajeev Verma; Eric T. Nalisnick; Christian A. Naesseth (UAI, 2025)

SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations. - Grigory Bartosh; Dmitry P. Vetrov; Christian A. Naesseth (CoRR, 2025)

Controlled Generation with Equivariant Variational Flow Matching. - Floor Eijkelboom; Heiko Zimmermann; Sharvaree Vadgama; Erik J. Bekkers; Max Welling; Christian A. Naesseth; Jan-Willem van de Meent (CoRR, 2025)

Monitoring Risks in Test-Time Adaptation. - Mona Schirmer; Metod Jazbec; Christian A. Naesseth; Eric T. Nalisnick (CoRR, 2025)