Stefan Bauer

arxiv.org

A scalable gene network model of regulatory dynamics in single cells

Paul Bertin

Joseph D Viviano

Alejandro Tejada-Lapuerta

Weixu Wang

Fabian J. Theis

2025-03-24

ArXiv (prépublication)

arxiv.org

Learning Decision Trees as Amortized Structure Inference

Mohammed Mahfoud

Ghait Boukachab

Michał Koziarski

Alex Hernandez-Garcia

Nikolay Malkin

2025-03-04

ICLR.cc/2025/Workshop/FPI (poster)

Causal machine learning for single-cell genomics

Alejandro Tejada-Lapuerta

Paul Bertin

Hananeh Aliee

Fabian J. Theis

2023-10-22

ArXiv (prépublication)

arxiv.org

Neural Causal Structure Discovery from Interventions

Nan Rosemary Ke

Bernhard Schölkopf

Michael Curtis Mozer

Christopher Pal

Recent promising results have generated a surge of interest in continuous optimization methods for causal discovery from observational data.… (voir plus) However, there are theoretical limitations on the identifiability of underlying structures obtained solely from observational data. Interventional data, on the other hand, provides richer information about the underlying data-generating process. Nevertheless, extending and applying methods designed for observational data to include interventions is a challenging problem. To address this issue, we propose a general framework based on neural networks to develop models that incorporate both observational and interventional data. Notably, our method can handle the challenging and realistic scenario where the identity of the intervened upon variable is unknown. We evaluate our proposed approach in the context of graph recovery, both de novo and from a partially-known edge set. Our method achieves strong benchmark results on various structure learning tasks, including structure recovery of synthetic graphs as well as standard graphs from the Bayesian Network Repository.

2023-09-09

TMLR (accepté)

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

2023-06-18

ICML.cc/2023/Workshop/SPIGM (poster)

Learning Latent Structural Causal Models

Jithendaraa Subramanian

Yashas Annadani

Ivaxi Sheth

Nan Rosemary Ke

Tristan Deleu

D. Nowrouzezahrai

S Ebrahimi Kahou

Causal learning has long concerned itself with the accurate recovery of underlying causal mechanisms. Such causal modelling enables better e… (voir plus)xplanations of out-of-distribution data. Prior works on causal learning assume that the high-level causal variables are given. However, in machine learning tasks, one often operates on low-level data like image pixels or high-dimensional vectors. In such settings, the entire Structural Causal Model (SCM) -- structure, parameters, \textit{and} high-level causal variables -- is unobserved and needs to be learnt from low-level data. We treat this problem as Bayesian inference of the latent SCM, given low-level data. For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions. Experiments are performed on synthetic datasets and a causally generated image dataset to demonstrate the efficacy of our approach. We also perform image generation from unseen interventions, thereby verifying out of distribution generalization for the proposed causal model.

2022-10-23

ArXiv (prépublication)

On the Generalization and Adaption Performance of Causal Models

Nan Rosemary Ke

2022-07-19

ICML.cc/2022/Workshop/SCIS (poster)

Bayesian Structure Learning with Generative Flow Networks

In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian ne… (voir plus)tworks, from data. Defining such a distribution is very challenging, due to the combinatorially large sample space, and approximations based on MCMC are often required. Recently, a novel class of probabilistic models, called Generative Flow Networks (GFlowNets), have been introduced as a general framework for generative modeling of discrete and composite objects, such as graphs. In this work, we propose to use a GFlowNet as an alternative to MCMC for approximating the posterior distribution over the structure of Bayesian networks, given a dataset of observations. Generating a sample DAG from this approximate distribution is viewed as a sequential decision problem, where the graph is constructed one edge at a time, based on learned transition probabilities. Through evaluation on both simulated and real data, we show that our approach, called DAG-GFlowNet, provides an accurate approximation of the posterior over DAGs, and it compares favorably against other methods based on MCMC or variational inference.

2022-05-19

auai.org/UAI/2022/Conference (poster)

proceedings.mlr.press

From Points to Functions: Infinite-dimensional Representations in Diffusion Models

Sarthak Mittal

Guillaume Lajoie

Arash Mehrjou

Diffusion-based generative models learn to iteratively transfer unstructured noise to a complex target distribution as opposed to Generative… (voir plus) Adversarial Networks (GANs) or the decoder of Variational Autoencoders (VAEs) which produce samples from the target distribution in a single step. Thus, in diffusion models every sample is naturally connected to a random trajectory which is a solution to a learned stochastic differential equation (SDE). Generative models are only concerned with the final state of this trajectory that delivers samples from the desired distribution. Abstreiter et. al showed that these stochastic trajectories can be seen as continuous filters that wash out information along the way. Consequently, it is reasonable to ask if there is an intermediate time step at which the preserved information is optimal for a given downstream task. In this work, we show that a combination of information content from different time steps gives a strictly better representation for the downstream task. We introduce an attention and recurrence based modules that ``learn to mix'' information content of various time-steps such that the resultant representation leads to superior performance in downstream tasks.

2022-03-27

ICLR.cc/2022/Workshop/DGM4HSD (poster)

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning

Nan Rosemary Ke

Aniket Rajiv Didolkar

Danilo Jimenez Rezende

Michael Curtis Mozer

Christopher Pal

Inducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the premise tha… (voir plus)t the causal variables themselves are observed. However, for AI agents such as robots trying to make sense of their environment, the only observables are low-level variables like pixels in images. To generalize well, an agent must induce high-level variables, particularly those which are causal or are affected by causal variables. A central goal for AI and causality is thus the joint discovery of abstract representations and causal structure. However, we note that existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs which are impossible to manipulate parametrically (e.g., number of nodes, sparsity, causal chain length, etc.). In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them. In order to systematically probe the ability of methods to identify these variables and structures, we design a suite of benchmarking RL environments. We evaluate various representation learning algorithms from the literature and find that explicitly incorporating structure and modularity in models can help causal induction in model-based reinforcement learning.

2021-10-10

NeurIPS.cc/2021/Track/Datasets_and_Benchmarks/Round2 (publié)

Learning Neural Causal Models with Active Interventions

Nino Scherrer

Olexa Bilaniuk

Yashas Annadani

Anirudh Goyal

Patrick Schwab

Bernhard Schölkopf

Michael Curtis Mozer

Nan Rosemary Ke

Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science. The appealing … (voir plus)scaling properties of neural networks have recently led to a surge of interest in differentiable neural network-based methods for learning causal structures from data. So far, differentiable causal discovery has focused on static datasets of observational or interventional origin. In this work, we introduce an active intervention-targeting mechanism which enables quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting and is applicable for both discrete and continuous optimization formulations of learning the underlying directed acyclic graph (DAG) from data. We examine the proposed method across multiple frameworks in a wide range of settings and demonstrate superior performance on multiple benchmarks from simulated to real-world data.

2021-09-05

ArXiv (prépublication)