| Solving Schr\" odinger Bridges via Maximum Likelihood F Vargas, P Thodoroff, ND Lawrence, A Lamacraft Entropy 2021, 23, 1134. https://doi.org/10.3390/e23091134 23 (9), 2021 | 99 | 2021 |
| Denoising Diffusion Samplers F Vargas, W Grathwohl, A Doucet The Eleventh International Conference on Learning Representations, ICLR 2023, 2023 | 35 | 2023 |
| Kernelized Concept Erasure S Ravfogel, F Vargas, Y Goldberg, R Cotterell EMNLP 2022, 2022 | 30* | 2022 |
| Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation F Vargas, R Cotterell Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 28 | 2020 |
| Bayesian learning via neural Schrödinger–Föllmer flows F Vargas, A Ovsianas, D Fernandes, M Girolami, ND Lawrence, N Nüsken Statistics and Computing 33 (1), 1-22, 2023 | 27 | 2023 |
| Transport meets variational inference: Controlled monte carlo diffusions F Vargas, S Padhy, D Blessing, N Nüsken The Twelfth International Conference on Learning Representations, 2024 | 11* | 2024 |
| Shooting Schrödinger’s Cat DL Fernandes, F Vargas, CH Ek, NDF Campbell Fourth Symposium on Advances in Approximate Bayesian Inference, 2021 | 10 | 2021 |
| Machine-Learning Approaches for the Empirical Schrödinger Bridge Problem F Vargas https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-958.html, 2020 | 9 | 2020 |
| Device and method for natural language processing through statistical model comparison F Vargas, K Brestnichki, DG Sherburn, V ZHELEZNIAK, NY Hammerla US Patent 10,482,183, 2019 | 6 | 2019 |
| To smooth a cloud or to pin it down: Expressiveness guarantees and insights on score matching in denoising diffusion models T Reu, F Vargas, A Kerekes, MM Bronstein The 40th Conference on Uncertainty in Artificial Intelligence, 0 | 6* | |
| A framework for conditional diffusion modelling with applications in motif scaffolding for protein design K Didi, F Vargas, SV Mathis, V Dutordoir, E Mathieu, UJ Komorowska, ... arXiv preprint arXiv:2312.09236, 2023 | 4 | 2023 |
| DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised -transform A Denker, F Vargas, S Padhy, K Didi, S Mathis, V Dutordoir, R Barbano, ... arXiv preprint arXiv:2406.01781, 2024 | 1 | 2024 |
| Dynamics-Informed Protein Design with Structure Conditioning UJ Komorowska, SV Mathis, K Didi, F Vargas, P Lio, M Jamnik The Twelfth International Conference on Learning Representations, 2023 | 1 | 2023 |
| Dimensionality Reduction as Probabilistic Inference A Ravuri, F Vargas, V Lalchand, ND Lawrence arXiv preprint arXiv:2304.07658, 2023 | 1 | 2023 |
| Multilingual Factor Analysis F Vargas, K Brestnichki, A Papadopoulos-Korfiatis, N Hammerla Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 1 | 2019 |
| Model Comparison for Semantic Grouping F Vargas, K Brestnichki, N Hammerla Proceedings of the 36th International Conference on Machine Learning., 2018 | 1 | 2018 |
| Improving Antibody Design with Force-Guided Sampling in Diffusion Models P Kulytė, F Vargas, SV Mathis, YG Wang, JM Hernández-Lobato, P Liò arXiv preprint arXiv:2406.05832, 2024 | | 2024 |
| Efficient privacy-preserving inference for convolutional neural networks H Xuanyuan, F Vargas, S Cummins ICLR 2022 Workshop on PAIR {\textasciicircum} 2Struct: Privacy …, 2021 | | 2021 |
| 5.7 The Schrödinger bridge problem F Vargas Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling …, 0 | | |
Neil D. LawrenceUniversity of CambridgeVerified email at cl.cam.ac.uk
