Francisco Vargas

According to our database, Francisco Vargas authored at least 26 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Path Integral Optimiser: Global Optimisation via Neural Schrödinger-Föllmer Diffusion.

Max McGuinness, Eirik Fladmark, Francisco Vargas

CoRR, June, 2025

RNE: a plug-and-play framework for diffusion density estimation and inference-time control.

Jiajun He, José Miguel Hernández-Lobato, Yuanqi Du, Francisco Vargas

CoRR, June, 2025

FEAT: Free energy Estimators with Adaptive Transport.

Jiajun He, Yuanqi Du, Francisco Vargas, Yuanqing Wang, Carla P. Gomes, José Miguel Hernández-Lobato, Eric Vanden-Eijnden

CoRR, April, 2025

No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers.

Jiajun He, Yuanqi Du, Francisco Vargas, Dinghuai Zhang, Shreyas Padhy, Ruikang Ouyang, Carla P. Gomes, José Miguel Hernández-Lobato

CoRR, February, 2025

Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo.

Cheuk Kit Lee, Paul Jeha, Jes Frellsen, Pietro Lio, Michael Samuel Albergo, Francisco Vargas

CoRR, February, 2025

Iterative Importance Fine-tuning of Diffusion Models.

Alexander Denker, Shreyas Padhy, Francisco Vargas, Johannes Hertrich

CoRR, February, 2025

2024

Improving Antibody Design with Force-Guided Sampling in Diffusion Models.

Paulina Kulyte, Francisco Vargas, Simon Valentin Mathis, Yu Guang Wang, José Miguel Hernández-Lobato, Pietro Liò

CoRR, 2024

DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform.

Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

CoRR, 2024

To smooth a cloud or to pin it down: Expressiveness guarantees and insights on score matching in denoising diffusion models.

Teodora Reu, Francisco Vargas, Anna Kerekes, Michael M. Bronstein

Proceedings of the Uncertainty in Artificial Intelligence, 2024

DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform.

Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Riccardo Barbano, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lió

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling.

Denis Blessing, Xiaogang Jia, Johannes Esslinger, Francisco Vargas, Gerhard Neumann

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Dynamics-Informed Protein Design with Structure Conditioning.

Urszula Julia Komorowska, Simon V. Mathis, Kieran Didi, Francisco Vargas, Pietro Lio, Mateja Jamnik

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Transport meets Variational Inference: Controlled Monte Carlo Diffusions.

Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Correction: Vargas et al. Solving Schrödinger Bridges via Maximum Likelihood. Entropy 2021, 23, 1134.

Francisco Vargas, Pierre Thodoroff, Austen Lamacraft, Neil D. Lawrence

Entropy, February, 2023

Bayesian learning via neural Schrödinger-Föllmer flows.

Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken

Stat. Comput., 2023

A framework for conditional diffusion modelling with applications in motif scaffolding for protein design.

Kieran Didi, Francisco Vargas, Simon V. Mathis, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

CoRR, 2023

Transport, Variational Inference and Diffusions: with Applications to Annealed Flows and Schrödinger Bridges.

Francisco Vargas, Nikolas Nüsken

CoRR, 2023

Expressiveness Remarks for Denoising Diffusion Models and Samplers.

Francisco Vargas, Teodora Reu, Anna Kerekes

CoRR, 2023

Dimensionality Reduction as Probabilistic Inference.

Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence

CoRR, 2023

Denoising Diffusion Samplers.

Francisco Vargas, Will Sussman Grathwohl, Arnaud Doucet

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Adversarial Concept Erasure in Kernel Space.

Shauli Ravfogel, Francisco Vargas, Yoav Goldberg, Ryan Cotterell

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

2021

Solving Schrödinger Bridges via Maximum Likelihood.

Francisco Vargas, Pierre Thodoroff, Austen Lamacraft, Neil D. Lawrence

Entropy, 2021

Efficient Representations for Privacy-Preserving Inference.

Han Xuanyuan, Francisco Vargas, Stephen Cummins

CoRR, 2021

2020

Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation.

Francisco Vargas, Ryan Cotterell

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

2019

Model Comparison for Semantic Grouping.

Francisco Vargas, Kamen Brestnichki, Nils Hammerla

Proceedings of the 36th International Conference on Machine Learning, 2019

Multilingual Factor Analysis.

Francisco Vargas, Kamen Brestnichki, Alex Papadopoulos-Korfiatis, Nils Hammerla

Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019