1 code implementation • 26 Oct 2023 • Lars Lorch, Andreas Krause, Bernhard Schölkopf
We develop a novel approach towards 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.
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.
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.
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.
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.
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.
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.
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.
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.