Search results
Results: 17
Number of items: 17
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Cornet, F., Bartosh, G., Schmidt, M. N., & Naesseth, C. A. (2025). Equivariant Neural Diffusion for Molecule Generation. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), 38th Conference on Neural Information Processing Systems (NeurIPS 2024): 10-15 December 2024, Vancouver, Canada (pp. 49429-49460). (Advances in Neural Information Processing Systems; Vol. 37). Neural Information Processing Systems Foundation. https://doi.org/10.52202/079017-1564 -
Schirmer, M., Jazbec, M., Naesseth, C. A., & Nalisnick, E. (2025). Monitoring Risks in Test-Time Adaptation. In D. Belgrave, C. Zhang, H. Lin, R. Pascanu, P. Koniusz, M. Ghassemi, & N. Chen (Eds.), 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025): 2-7 December 2025, San Diego, California, USA and 30 November-5 December 2025, Mexico City, Mexico (pp. 89783-89816). (Advances in Neural Information Processing Systems; Vol. 38). Neural Information Processing Systems Foundation. https://doi.org/10.52202/085713-2705
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Bartosh, G., Vetrov, D., & Naesseth, C. A. (2025). Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), 38th Conference on Neural Information Processing Systems (NeurIPS 2024): 10-15 December 2024, Vancouver, Canada (pp. 73952-73985). (Advances in Neural Information Processing Systems; Vol. 37). Neural Information Processing Systems Foundation. https://doi.org/10.52202/079017-2352
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Bartosh, G., Vetrov, D., & Naesseth, C. A. (2025). SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations. Proceedings of Machine Learning Research, 267, 3054-3070. https://proceedings.mlr.press/v267/bartosh25a.html
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Timans, A., Verma, R., Nalisnick, E., & Naesseth, C. A. (2025). On Continuous Monitoring of Risk Violations under Unknown Shift. Proceedings of Machine Learning Research, 286, 4204-4215. https://proceedings.mlr.press/v286/timans25a.html
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Eijkelboom, F., Bartosh, G., Naesseth, C. A., Welling, M., & van de Meent, J.-W. (2025). Variational Flow Matching for Graph Generation. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), 38th Conference on Neural Information Processing Systems (NeurIPS 2024): 10-15 December 2024, Vancouver, Canada (pp. 11735-11764). (Advances in Neural Information Processing Systems; Vol. 37). Neural Information Processing Systems Foundation. https://doi.org/10.52202/079017-0374
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Zimmermann, H., Naesseth, C. A., & van de Meent, J.-W. (2025). VISA: Variational Inference with Sequential Sample-Average Approximations. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), 38th Conference on Neural Information Processing Systems (NeurIPS 2024): 10-15 December 2024, Vancouver, Canada (pp. 138789-138808). (Advances in Neural Information Processing Systems; Vol. 37). Neural Information Processing Systems Foundation. https://doi.org/10.52202/079017-4403
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Timans, A., Straehle, C.-N., Sakmann, K., Naesseth, C. A., & Nalisnick, E. (2025). Max-Rank: Efficient Multiple Testing for Conformal Prediction. Proceedings of Machine Learning Research, 258, 3898-3906. https://proceedings.mlr.press/v258/timans25a.html
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Eijkelboom, F., Zimmermann, H., Vadgama, S., Bekkers, E. J., Welling, M., Naesseth, C. A., & van de Meent, J.-W. (2025). Controlled Generation with Equivariant Variational Flow Matching. Proceedings of Machine Learning Research, 267, 15066-15078. https://proceedings.mlr.press/v267/eijkelboom25a.html
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