Naesseth, Christian A.

26 publications

ICML 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

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

NeurIPS 2025 Monitoring Risks in Test-Time Adaptation Mona Schirmer, Metod Jazbec, Christian A. Naesseth, Eric Nalisnick

UAI 2025 On Continuous Monitoring of Risk Violations Under Unknown Shift Alexander Timans, Rajeev Verma, Eric Nalisnick, Christian A. Naesseth

NeurIPS 2025 Reverse Diffusion Sequential Monte Carlo Samplers Luhuan Wu, Yi Han, Christian A. Naesseth, John Patrick Cunningham

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

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

AISTATS 2025 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

TMLR 2024 E-Valuating Classifier Two-Sample Tests Teodora Pandeva, Tim Bakker, Christian A. Naesseth, Patrick Forré

NeurIPS 2024 Equivariant Neural Diffusion for Molecule Generation François Cornet, Grigory Bartosh, Mikkel N. Schmidt, Christian A. Naesseth

ICMLW 2024 Equivariant Neural Diffusion for Molecule Generation François R J Cornet, Grigory Bartosh, Mikkel N. Schmidt, Christian A. Naesseth

NeurIPS 2024 Fast yet Safe: Early-Exiting with Risk Control Metod Jazbec, Alexander Timans, Tin Hadži Veljković, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, Eric Nalisnick

ICMLW 2024 Fast yet Safe: Early-Exiting with Risk Control Metod Jazbec, Alexander Timans, Tin Hadži Veljković, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, Eric Nalisnick

ICMLW 2024 Fast yet Safe: Early-Exiting with Risk Control Metod Jazbec, Alexander Timans, Tin Hadži Veljković, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, Eric Nalisnick

ICML 2024 Neural Diffusion Models Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth

NeurIPS 2024 Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth

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

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

TMLR 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

TMLR 2023 A Variational Perspective on Generative Flow Networks Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A Naesseth

ICMLW 2023 Practical and Asymptotically Exact Conditional Sampling in Diffusion Models Brian L. Trippe, Luhuan Wu, Christian A. Naesseth, David Blei, John Patrick Cunningham

TMLR 2023 Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport Liyi Zhang, David Blei, Christian A Naesseth

UAI 2021 Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David Blei, Itsik Pe’er

FnTML 2019 Elements of Sequential Monte Carlo Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

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

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