Lars Lorch

• PhD Student
• lars.lorch@inf.ethz.ch
• CAB G66
• LinkedIn

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Publications

2023

BaCaDI: Bayesian Causal Discovery with Unknown Interventions A. Hägele, J. Rothfuss, L. Lorch, V. R. Somnath, B. Schölkopf, A. Krause In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), 2023 Oral presentation [bibtex] [abstract] [pdf]

2022

Amortized Inference for Causal Structure Learning L. Lorch, S. Sussex, J. Rothfuss, A. Krause, B. Schölkopf In Proc. Neural Information Processing Systems (NeurIPS), 2022 [bibtex] [abstract] [pdf]

Active Bayesian Causal Inference C. Toth, L. Lorch, C. Knoll, A. Krause, F. Pernkopf, R. Peharz, J. von Kügelgen In Proc. Neural Information Processing Systems (NeurIPS), 2022 [bibtex] [abstract] [pdf]

Listening to Bluetooth beacons for epidemic risk mitigation G. Barthe, R. D. Viti, P. Druschel, D. Garg, M. Gomez-Rodriguez, P. Ingo, H. Kremer, M. Lentz, L. Lorch, A. Mehta, others In Scientific reports, Nature Publishing Group, volume 12, 2022 [bibtex] [abstract] [pdf]

Pooled testing of traced contacts under superspreading dynamics S. Tsirtsis, A. De, L. Lorch, M. Gomez-Rodriguez In PLoS computational biology, volume 18, 2022 [bibtex] [abstract] [pdf]

Quantifying the effects of contact tracing, testing, and containment measures in the presence of infection hotspots L. Lorch, H. Kremer, W. Trouleau, S. Tsirtsis, A. Szanto, B. Schölkopf, M. Gomez-Rodriguez In ACM Transactions on Spatial Systems and Algorithms, 2022 [bibtex] [abstract] [pdf]

2021

DiBS: Differentiable Bayesian Structure Learning L. Lorch, J. Rothfuss, B. Schölkopf, A. Krause In Proc. Neural Information Processing Systems (NeurIPS), 2021 [bibtex] [abstract] [pdf] [code] [blog]

2020

Incorporating interpretable output constraints in Bayesian neural networks W. Yang, L. Lorch, M. Graule, H. Lakkaraju, F. Doshi-Velez In Advances in Neural Information Processing Systems, volume 33, 2020 [bibtex] [abstract] [pdf]

2019

Output-constrained Bayesian neural networks W. Yang, L. Lorch, M. A. Graule, S. Srinivasan, A. Suresh, J. Yao, M. F. Pradier, F. Doshi-Velez In ICML 2019 Workshops on Uncertainty and Robustness in Deep Learning and on Understanding and Improving Generalization in Deep Learning, 2019 [bibtex] [abstract] [pdf]

2018

Stochastic optimal control of epidemic processes in networks L. Lorch, A. De, S. Bhatt, W. Trouleau, U. Upadhyay, M. Gomez-Rodriguez In NeurIPS 2018 Workshop on Machine Learning for Health (ML4H), 2018 [bibtex] [abstract] [pdf]