41 citations · 43 across the 4 of their papers we have counts for
7 papers
Learning Identifiable Factorized Causal Representations of Cellular Responses
Haiyi Mao, Romain Lopez, Kai Liu +4
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…
Toward the Identifiability of Comparative Deep Generative Models
Romain Lopez, Jan-Christian Huetter, Ehsan Hajiramezanali +2
Deep Generative Models (DGMs) are versatile tools for learning data representations while adequately incorporating domain knowledge such as the specification of conditional probabi…
Estimation Rates for Sparse Linear Cyclic Causal Models
Jan-Christian Hütter, Philippe Rigollet
Causal models are important tools to understand complex phenomena and predict the outcome of controlled experiments, also known as interventions. In this work, we present statistic…
Estimation of Monge Matrices
Jan-Christian Hütter, Cheng Mao, Philippe Rigollet +1
Monge matrices and their permuted versions known as pre-Monge matrices naturally appear in many domains across science and engineering. While the rich structural properties of such…
Statistical Optimal Transport via Factored Couplings
Aden Forrow, Jan-Christian Hütter, Mor Nitzan +3
We propose a new method to estimate Wasserstein distances and optimal transport plans between two probability distributions from samples in high dimension. Unlike plug-in rules tha…
Asymptotic Behavior of Gradient Flows Driven by Nonlocal Power Repulsion and Attraction Potentials in One Dimension
Marco Di Francesco, Massimo Fornasier, Jan-Christian Hütter +1
We study the long time behavior of the Wasserstein gradient flow for an energy functional consisting of two components: particles are attracted to a fixed profile by means of a…
Consistency of Probability Measure Quantization by Means of Power Repulsion-Attraction Potentials
Massimo Fornasier, Jan-Christian Hütter
This paper is concerned with the study of the consistency of a variational method for probability measure quantization, deterministically realized by means of a minimizing principl…