Raycast based auto-rigging method for humanoid meshes. - Romain Lopez; Christophe Poirel (SIGGRAPH Posters, 2013)

A deep generative model for gene expression profiles from single-cell RNA sequencing. - Romain Lopez; Jeffrey Regier; Michael I. Jordan; Nir Yosef (CoRR, 2017)

Information Constraints on Auto-Encoding Variational Bayes. - Romain Lopez; Jeffrey Regier; Michael I. Jordan; Nir Yosef (NeurIPS, 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)

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)

Cost-Effective Incentive Allocation via Structured Counterfactual Inference. - Romain Lopez; Chenchen Li; Xiang Yan; Junwu Xiong; Michael I. Jordan; Yuan Qi 001; Le Song (AAAI, 2020)

Decision-Making with Auto-Encoding Variational Bayes. - Romain Lopez; Pierre Boyeau; Nir Yosef; Michael I. Jordan; Jeffrey Regier (NeurIPS, 2020)

Learning from eXtreme Bandit Feedback. - Romain Lopez; Inderjit S. Dhillon; Michael I. Jordan (AAAI, 2021)

Charting Cellular States, One Cell at a Time: Computational, Inferential and Modeling Perspectives. - Romain Lopez (2021)

Disentangling shared and group-specific variations in single-cell transcriptomics data with multiGroupVI. - Ethan Weinberger; Romain Lopez; Jan-Christian Hütter; Aviv Regev (MLCB, 2022)

Large-Scale Differentiable Causal Discovery of Factor Graphs. - Romain Lopez; Jan-Christian Hütter; Jonathan K. Pritchard; Aviv Regev (NeurIPS, 2022)

NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning. - Muralikrishnna G. Sethuraman; Romain Lopez; Rahul Mohan; Faramarz Fekri; Tommaso Biancalani; Jan-Christian Hütter (AISTATS, 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 (CLeaR, 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 (MLCB, 2023)

Toward the Identifiability of Comparative Deep Generative Models. - Romain Lopez; Jan-Christian Hütter; Ehsan Hajiramezanali; Jonathan K. Pritchard; Aviv Regev (CLeaR, 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 (CVPR Workshops, 2024)

GFlowNet Assisted Biological Sequence Editing. - Pouya M. Ghari; Alex M. Tseng; Gökcen Eraslan; Romain Lopez; Tommaso Biancalani; Gabriele Scalia; Ehsan Hajiramezanali (NeurIPS, 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 (NeurIPS, 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 (RECOMB, 2024)

Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport. - Jayoung Ryu; Charlotte Bunne; Luca Pinello; Aviv Regev; Romain Lopez (AISTATS, 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 (ICLR, 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; et al. (CoRR, 2025)