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2020 – today
- 2024
[c13]Romain Lopez, Jan-Christian Hütter, Ehsan Hajiramezanali, Jonathan K. Pritchard, Aviv Regev:
Toward the Identifiability of Comparative Deep Generative Models. CLeaR 2024: 868-912- [c12]Mahtab Bigverdi, Burkhard Höckendorf, Heming Yao, Phil Hanslovsky, Romain Lopez, David Richmond:
Gene-Level Representation Learning via Interventional Style Transfer in Optical Pooled Screening. CVPR Workshops 2024: 7921-7931 - [c11]Kexin Huang, Romain Lopez, Jan-Christian Hütter, Takamasa Kudo, Antonio Rios, Aviv Regev:
Sequential Optimal Experimental Design of Perturbation Screens Guided by Multi-modal Priors. RECOMB 2024: 17-37 - [i13]Romain Lopez, Jan-Christian Hütter, Ehsan Hajiramezanali, Jonathan K. Pritchard, Aviv Regev:
Toward the Identifiability of Comparative Deep Generative Models. CoRR abs/2401.15903 (2024) - [i12]Mahtab Bigverdi, Burkhard Hockendorf, Heming Yao, Phil Hanslovsky, Romain Lopez, David Richmond:
Gene-Level Representation Learning via Interventional Style Transfer in Optical Pooled Screening. CoRR abs/2406.07763 (2024) - [i11]Haiyi Mao, Romain Lopez, Kai Liu, Jan-Christian Hütter, David Richmond, Panayiotis V. Benos, Lin Qiu:
Learning Identifiable Factorized Causal Representations of Cellular Responses. CoRR abs/2410.22472 (2024) - 2023
- [c10]Muralikrishnna G. Sethuraman, Romain Lopez, Rahul Mohan, Faramarz Fekri
, Tommaso Biancalani, Jan-Christian Hütter:
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning. AISTATS 2023: 6371-6387 - [c9]Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan K. Pritchard, Aviv Regev:
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling. CLeaR 2023: 662-691 - [c8]Xinming Tu, Jan-Christian Hütter, Zitong Jerry Wang, Takamasa Kudo, Aviv Regev, Romain Lopez:
A Supervised Contrastive Framework for Learning Disentangled Representations of Cellular Perturbation Data. MLCB 2023: 90-100 - [i10]Muralikrishnna G. Sethuraman, Romain Lopez, Rahul Mohan, Faramarz Fekri, Tommaso Biancalani, Jan-Christian Hütter:
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning. CoRR abs/2301.01849 (2023) - 2022
- [c7]Ethan Weinberger, Romain Lopez, Jan-Christian Hütter, Aviv Regev:
Disentangling shared and group-specific variations in single-cell transcriptomics data with multiGroupVI. MLCB 2022: 16-32 - [c6]Romain Lopez, Jan-Christian Hütter, Jonathan K. Pritchard, Aviv Regev:
Large-Scale Differentiable Causal Discovery of Factor Graphs. NeurIPS 2022 - [i9]Romain Lopez, Jan-Christian Hütter, Jonathan K. Pritchard, Aviv Regev:
Large-Scale Differentiable Causal Discovery of Factor Graphs. CoRR abs/2206.07824 (2022) - [i8]Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan K. Pritchard, Aviv Regev:
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling. CoRR abs/2211.03553 (2022) - 2021
- [b1]Romain Lopez:
Charting Cellular States, One Cell at a Time: Computational, Inferential and Modeling Perspectives. University of California, Berkeley, USA, 2021 - [c5]Romain Lopez, Inderjit S. Dhillon, Michael I. Jordan:
Learning from eXtreme Bandit Feedback. AAAI 2021: 8732-8740 - 2020
- [c4]Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi, Le Song:
Cost-Effective Incentive Allocation via Structured Counterfactual Inference. AAAI 2020: 4997-5004 - [c3]Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier:
Decision-Making with Auto-Encoding Variational Bayes. NeurIPS 2020 - [i7]Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier:
Decision-Making with Auto-Encoding Variational Bayes. CoRR abs/2002.07217 (2020) - [i6]Romain Lopez, Inderjit S. Dhillon, Michael I. Jordan:
Learning from eXtreme Bandit Feedback. CoRR abs/2009.12947 (2020)
2010 – 2019
- 2019
- [i5]Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi, Le Song:
Cost-Effective Incentive Allocation via Structured Counterfactual Inference. CoRR abs/1902.02495 (2019) - [i4]Romain Lopez, Achille Nazaret, Maxime Langevin, Jules Samaran, Jeffrey Regier, Michael I. Jordan, Nir Yosef:
A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. CoRR abs/1905.02269 (2019) - 2018
- [c2]Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef:
Information Constraints on Auto-Encoding Variational Bayes. NeurIPS 2018: 6117-6128 - [i3]Romain Lopez, Jeffrey Regier, Nir Yosef, Michael I. Jordan:
Information Constraints on Auto-Encoding Variational Bayes. CoRR abs/1805.08672 (2018) - [i2]Maxime Langevin, Edouard Mehlman, Jeffrey Regier, Romain Lopez, Michael I. Jordan, Nir Yosef:
A Deep Generative Model for Semi-Supervised Classification with Noisy Labels. CoRR abs/1809.05957 (2018) - 2017
- [i1]Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef:
A deep generative model for gene expression profiles from single-cell RNA sequencing. CoRR abs/1709.02082 (2017) - 2013
- [c1]Romain Lopez, Christophe Poirel:
Raycast based auto-rigging method for humanoid meshes. SIGGRAPH Posters 2013: 11
Coauthor Index
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