Search results
Results: 11
Number of items: 11
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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 -
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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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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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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Yang, H., Moretti, A. K., Macaluso, S., Chlenski, P., Naesseth, C. A., & Pe’er, I. (2024). Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics. Transactions on Machine Learning Research, 2024, Article 2926. https://doi.org/10.48550/arXiv.2406.03242
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Bartosh, G., Vetrov, D., & Naesseth, C. A. (2024). Neural Diffusion Models. Proceedings of Machine Learning Research, 235, 3073-3095. https://proceedings.mlr.press/v235/bartosh24a.html
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Wu, L., Trippe, B. L., Naesseth, C. A., Blei, D. M., & Cunningham, J. P. (2023). Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), 37th Conference on Neural Information Processing Systems (NeurIPS 2023): 10-16 December 2023, New Orleans, Louisana, USA (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2306.17775
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Zhang, L., Blei, D., & Naesseth, C. A. (2023). Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport. Transactions on Machine Learning Research, 2023, Article 1118. https://openreview.net/forum?id=7KW7zvKd7J
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Zimmermann, H., Lindsten, F., van de Meent, J.-W., & Naesseth, C. A. (2023). A Variational Perspective on Generative Flow Networks. Transactions on Machine Learning Research, 2023, Article 612. https://openreview.net/forum?id=AZ4GobeSLq
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