Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Lopez, Romain"'
Autor:
Mao, Haiyi, Lopez, Romain, Liu, Kai, Huetter, Jan-Christian, Richmond, David, Benos, Panayiotis V., Qiu, Lin
The study of cells and their responses to genetic or chemical perturbations promises to accelerate the discovery of therapeutic targets. However, designing adequate and insightful models for such data is difficult because the response of a cell to pe
Externí odkaz:
http://arxiv.org/abs/2410.22472
Autor:
Bigverdi, Mahtab, Hockendorf, Burkhard, Yao, Heming, Hanslovsky, Phil, Lopez, Romain, Richmond, David
Optical pooled screening (OPS) combines automated microscopy and genetic perturbations to systematically study gene function in a scalable and cost-effective way. Leveraging the resulting data requires extracting biologically informative representati
Externí odkaz:
http://arxiv.org/abs/2406.07763
It is now possible to conduct large scale perturbation screens with complex readout modalities, such as different molecular profiles or high content cell images. While these open the way for systematic dissection of causal cell circuits, integrated s
Externí odkaz:
http://arxiv.org/abs/2405.00838
Autor:
Lopez, Romain, Huetter, Jan-Christian, Hajiramezanali, Ehsan, Pritchard, Jonathan, Regev, Aviv
Publikováno v:
Causal Learning and Reasoning 2024
Deep Generative Models (DGMs) are versatile tools for learning data representations while adequately incorporating domain knowledge such as the specification of conditional probability distributions. Recently proposed DGMs tackle the important task o
Externí odkaz:
http://arxiv.org/abs/2401.15903
Autor:
Sethuraman, Muralikrishnna G., Lopez, Romain, Mohan, Rahul, Fekri, Faramarz, Biancalani, Tommaso, Hütter, Jan-Christian
Learning causal relationships between variables is a well-studied problem in statistics, with many important applications in science. However, modeling real-world systems remain challenging, as most existing algorithms assume that the underlying caus
Externí odkaz:
http://arxiv.org/abs/2301.01849
Autor:
Lopez, Romain, Tagasovska, Nataša, Ra, Stephen, Cho, Kyunghyn, Pritchard, Jonathan K., Regev, Aviv
Latent variable models such as the Variational Auto-Encoder (VAE) have become a go-to tool for analyzing biological data, especially in the field of single-cell genomics. One remaining challenge is the interpretability of latent variables as biologic
Externí odkaz:
http://arxiv.org/abs/2211.03553
Publikováno v:
Advances in Neural Information Processing Systems 35 (2022)
A common theme in causal inference is learning causal relationships between observed variables, also known as causal discovery. This is usually a daunting task, given the large number of candidate causal graphs and the combinatorial nature of the sea
Externí odkaz:
http://arxiv.org/abs/2206.07824
Publikováno v:
AAAI Conference on Artificial Intelligence 2021
We study the problem of batch learning from bandit feedback in the setting of extremely large action spaces. Learning from extreme bandit feedback is ubiquitous in recommendation systems, in which billions of decisions are made over sets consisting o
Externí odkaz:
http://arxiv.org/abs/2009.12947
Publikováno v:
Advances in Neural Information Processing Systems 2020
To make decisions based on a model fit with auto-encoding variational Bayes (AEVB), practitioners often let the variational distribution serve as a surrogate for the posterior distribution. This approach yields biased estimates of the expected risk,
Externí odkaz:
http://arxiv.org/abs/2002.07217
Autor:
Lopez, Romain, Nazaret, Achille, Langevin, Maxime, Samaran, Jules, Regier, Jeffrey, Jordan, Michael I., Yosef, Nir
Spatial studies of transcriptome provide biologists with gene expression maps of heterogeneous and complex tissues. However, most experimental protocols for spatial transcriptomics suffer from the need to select beforehand a small fraction of genes t
Externí odkaz:
http://arxiv.org/abs/1905.02269