Causal Modeling with Stationary Diffusions

1 code implementation • 26 Oct 2023 • Lars Lorch, Andreas Krause, Bernhard Schölkopf

We develop a novel approach towards causal inference.

Causal Inference

Active Bayesian Causal Inference

1 code implementation • 4 Jun 2022 • Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen

In this work, we propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active learning framework for integrated causal discovery and reasoning, which jointly infers a posterior over causal models and queries of interest.

Active Learning Causal Discovery +2

BaCaDI: Bayesian Causal Discovery with Unknown Interventions

1 code implementation • 3 Jun 2022 • Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, Andreas Krause

Inferring causal structures from experimentation is a central task in many domains.

Causal Discovery Variational Inference

Amortized Inference for Causal Structure Learning

1 code implementation • 25 May 2022 • Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf

Rather than searching over structures, we train a variational inference model to directly predict the causal structure from observational or interventional data.

Causal Discovery Inductive Bias +1

Group Testing under Superspreading Dynamics

1 code implementation • 30 Jun 2021 • Stratis Tsirtsis, Abir De, Lars Lorch, Manuel Gomez-Rodriguez

Testing is recommended for all close contacts of confirmed COVID-19 patients.

DiBS: Differentiable Bayesian Structure Learning

2 code implementations • NeurIPS 2021 • Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause

In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation.

Causal Discovery Variational Inference

Incorporating Interpretable Output Constraints in Bayesian Neural Networks

1 code implementation • NeurIPS 2020 • Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez

Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness.

Fairness Uncertainty Quantification

Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots

2 code implementations • 15 Apr 2020 • Lars Lorch, Heiner Kremer, William Trouleau, Stratis Tsirtsis, Aron Szanto, Bernhard Schölkopf, Manuel Gomez-Rodriguez

Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19.

Bayesian Optimization Point Processes

Output-Constrained Bayesian Neural Networks

1 code implementation • 15 May 2019 • Wanqian Yang, Lars Lorch, Moritz A. Graule, Srivatsan Srinivasan, Anirudh Suresh, Jiayu Yao, Melanie F. Pradier, Finale Doshi-Velez

Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space.

Stochastic Optimal Control of Epidemic Processes in Networks

no code implementations • 30 Oct 2018 • Lars Lorch, Abir De, Samir Bhatt, William Trouleau, Utkarsh Upadhyay, Manuel Gomez-Rodriguez

We approach the development of models and control strategies of susceptible-infected-susceptible (SIS) epidemic processes from the perspective of marked temporal point processes and stochastic optimal control of stochastic differential equations (SDEs) with jumps.

Point Processes