Romain Lopez

According to our database, Romain Lopez authored at least 22 papers between 2013 and 2025.

Collaborative distances:

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

2025

Supervised Contrastive Block Disentanglement.

Taro Makino, Ji Won Park, Natasa Tagasovska, Takamasa Kudo, Paula Coelho, Jan-Christian Huetter, Heming Yao, Burkhard Hoeckendorf, Ana Carolina Leote, Stephen Ra, David Richmond, Kyunghyun Cho, Aviv Regev, Romain Lopez

CoRR, February, 2025

Modeling Complex System Dynamics with Flow Matching Across Time and Conditions.

Martin Rohbeck, Edward De Brouwer, Charlotte Bunne, Jan-Christian Huetter, Anne Biton, Kelvin Y. Chen, Aviv Regev, Romain Lopez

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport.

Jayoung Ryu, Charlotte Bunne, Luca Pinello, Aviv Regev, Romain Lopez

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Sequential Optimal Experimental Design of Perturbation Screens Guided by Multi-modal Priors.

Kexin Huang, Romain Lopez, Jan-Christian Hütter, Takamasa Kudo, Antonio Rios, Aviv Regev

Proceedings of the Research in Computational Molecular Biology, 2024

Learning Identifiable Factorized Causal Representations of Cellular Responses.

Haiyi Mao, Romain Lopez, Kai Liu, Jan-Christian Huetter, David Richmond, Panayiotis V. Benos, Lin Qiu

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

GFlowNet Assisted Biological Sequence Editing.

Pouya M. Ghari, Alex M. Tseng, Gökcen Eraslan, Romain Lopez, Tommaso Biancalani, Gabriele Scalia, Ehsan Hajiramezanali

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

Gene-Level Representation Learning via Interventional Style Transfer in Optical Pooled Screening.

Mahtab Bigverdi, Burkhard Höckendorf, Heming Yao, Phil Hanslovsky, Romain Lopez, David Richmond

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Toward the Identifiability of Comparative Deep Generative Models.

Romain Lopez, Jan-Christian Hütter, Ehsan Hajiramezanali, Jonathan K. Pritchard, Aviv Regev

Proceedings of the Causal Learning and Reasoning, 2024

2023

A Supervised Contrastive Framework for Learning Disentangled Representations of Cellular Perturbation Data.

Xinming Tu, Jan-Christian Hütter, Zitong Jerry Wang, Takamasa Kudo, Aviv Regev, Romain Lopez

Proceedings of the Machine Learning in Computational Biology, November 30, 2023

Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling.

Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan K. Pritchard, Aviv Regev

Proceedings of the Conference on Causal Learning and Reasoning, 2023

NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning.

Muralikrishnna G. Sethuraman, Romain Lopez, Rahul Mohan, Faramarz Fekri, Tommaso Biancalani, Jan-Christian Hütter

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Large-Scale Differentiable Causal Discovery of Factor Graphs.

Romain Lopez, Jan-Christian Hütter, Jonathan K. Pritchard, Aviv Regev

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Disentangling shared and group-specific variations in single-cell transcriptomics data with multiGroupVI.

Ethan Weinberger, Romain Lopez, Jan-Christian Hütter, Aviv Regev

Proceedings of the Machine Learning in Computational Biology, 21-22 November 2022, Online, 2022

2021

Charting Cellular States, One Cell at a Time: Computational, Inferential and Modeling Perspectives.

Romain Lopez

PhD thesis, 2021

Learning from eXtreme Bandit Feedback.

Romain Lopez, Inderjit S. Dhillon, Michael I. Jordan

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Decision-Making with Auto-Encoding Variational Bayes.

Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Cost-Effective Incentive Allocation via Structured Counterfactual Inference.

Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi, Le Song

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements.

Romain Lopez, Achille Nazaret, Maxime Langevin, Jules Samaran, Jeffrey Regier, Michael I. Jordan, Nir Yosef

CoRR, 2019

2018

A Deep Generative Model for Semi-Supervised Classification with Noisy Labels.

Maxime Langevin, Edouard Mehlman, Jeffrey Regier, Romain Lopez, Michael I. Jordan, Nir Yosef

CoRR, 2018

Information Constraints on Auto-Encoding Variational Bayes.

Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

A deep generative model for gene expression profiles from single-cell RNA sequencing.

Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef

CoRR, 2017

2013

Raycast based auto-rigging method for humanoid meshes.

Romain Lopez, Christophe Poirel

Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference, 2013