Lars Lorch | Learning & Adaptive Systems Group

Lars Lorch

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

Publications

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]
BaCaDI: Bayesian Causal Discovery with Unknown Interventions A. Hägele, J. Rothfuss, L. Lorch, V. R. Somnath, B. Schölkopf, A. Krause In UAI 2022 Workshop on Causal Representation Learning, 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]