56 citations · 256 across the 37 of their papers we have counts for
39 papers
ARO: Aligned Representation learning for multi-Omics data
Amogh Singh, Yash Shah, Chiara D'Ercoli +4
The high cost of functional molecular assays, and prevalence of missing modalities and unmatched samples in computational biology, create significant barriers to comprehensive mult…
Theoretical Guarantees for Causal Discovery on Large Random Graphs
Mathieu Chevalley, Arash Mehrjou, Patrick Schwab
We investigate theoretical guarantees for the false-negative rate (FNR) -- the fraction of true causal edges whose orientation is not recovered, under single-variable random interv…
In-silico biological discovery with large perturbation models
Djordje Miladinovic, Tobias Höppe, Mathieu Chevalley +6
Data generated in perturbation experiments link perturbations to the changes they elicit and therefore contain information relevant to numerous biological discovery tasks -- from u…
Multi-megabase scale genome interpretation with genetic language models
Frederik Träuble, Lachlan Stuart, Andreas Georgiou +9
Understanding how molecular changes caused by genetic variation drive disease risk is crucial for deciphering disease mechanisms. However, interpreting genome sequences is challeng…
Efficient Differentiable Discovery of Causal Order
Mathieu Chevalley, Arash Mehrjou, Patrick Schwab
In the algorithm Intersort, Chevalley et al. (2024) proposed a score-based method to discover the causal order of variables in a Directed Acyclic Graph (DAG) model, leveraging inte…
Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
Mathieu Chevalley, Patrick Schwab, Arash Mehrjou
Targeted and uniform interventions to a system are crucial for unveiling causal relationships. While several methods have been developed to leverage interventional data for causal…
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design
Clare Lyle, Arash Mehrjou, Pascal Notin +4
The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms. Existing approaches search over the…
The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data
Mathieu Chevalley, Jacob Sackett-Sanders, Yusuf Roohani +15
In drug discovery, mapping interactions between genes within cellular systems is a crucial early step. Such maps are not only foundational for understanding the molecular mechanism…
Multi-omics Prediction from High-content Cellular Imaging with Deep Learning
Rahil Mehrizi, Arash Mehrjou, Maryana Alegro +10
High-content cellular imaging, transcriptomics, and proteomics data provide rich and complementary views on the molecular layers of biology that influence cellular states and funct…
Federated Causal Discovery From Interventions
Amin Abyaneh, Nino Scherrer, Patrick Schwab +3
Causal discovery serves a pivotal role in mitigating model uncertainty through recovering the underlying causal mechanisms among variables. In many practical domains, such as healt…
From Points to Functions: Infinite-dimensional Representations in Diffusion Models
Sarthak Mittal, Guillaume Lajoie, Stefan Bauer +1
Diffusion-based generative models learn to iteratively transfer unstructured noise to a complex target distribution as opposed to Generative Adversarial Networks (GANs) or the deco…
CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data
Mathieu Chevalley, Yusuf Roohani, Arash Mehrjou +2
Causal inference is a vital aspect of multiple scientific disciplines and is routinely applied to high-impact applications such as medicine. However, evaluating the performance of…
Diffusion Models for Video Prediction and Infilling
Tobias Höppe, Arash Mehrjou, Stefan Bauer +2
Predicting and anticipating future outcomes or reasoning about missing information in a sequence are critical skills for agents to be able to make intelligent decisions. This requi…
Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database
Arash Mehrjou, Ashkan Soleymani, Annika Buchholz +3
Federated learning (FL) has been proposed as a method to train a model on different units without exchanging data. This offers great opportunities in the healthcare sector, where l…
Physical Derivatives: Computing policy gradients by physical forward-propagation
Arash Mehrjou, Ashkan Soleymani, Stefan Bauer +1
Model-free and model-based reinforcement learning are two ends of a spectrum. Learning a good policy without a dynamic model can be prohibitively expensive. Learning the dynamic mo…
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL
Sumedh A Sontakke, Stephen Iota, Zizhao Hu +3
Out-of-distribution (OOD) detection is a well-studied topic in supervised learning. Extending the successes in supervised learning methods to the reinforcement learning (RL) settin…
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson +4
In vitro cellular experimentation with genetic interventions, using for example CRISPR technologies, is an essential step in early-stage drug discovery and target validation that s…
Federated Learning as a Mean-Field Game
Arash Mehrjou
We establish a connection between federated learning, a concept from machine learning, and mean-field games, a concept from game theory and control theory. In this analogy, the loc…
Diffusion-Based Representation Learning
Sarthak Mittal, Korbinian Abstreiter, Stefan Bauer +2
Diffusion-based methods represented as stochastic differential equations on a continuous-time domain have recently proven successful as a non-adversarial generative model. Training…
Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases
Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh +3
Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating…
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
Sumedh A. Sontakke, Arash Mehrjou, Laurent Itti +1
Animals exhibit an innate ability to learn regularities of the world through interaction. By performing experiments in their environment, they are able to discern the causal factor…
Real-time Prediction of COVID-19 related Mortality using Electronic Health Records
Patrick Schwab, Arash Mehrjou, Sonali Parbhoo +5
Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmis…
Learning Dynamical Systems using Local Stability Priors
Arash Mehrjou, Andrea Iannelli, Bernhard Schölkopf
A coupled computational approach to simultaneously learn a vector field and the region of attraction of an equilibrium point from generated trajectories of the system is proposed.…
Artificial Buildings: Safety, Complexity and a Quantifiable Measure of Beauty
Arash Mehrjou
A place to live is one of the most crucial necessities for all living organisms since the advent of life on planet Earth. The nature of homes has changed considerably over time. At…