Lars Lorch

• PhD Student
• lars.lorch@inf.ethz.ch
• OAT Y 17
• LinkedIn

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Publications

2025
Standardizing Structural Causal Models W. Ormaniec*, S. Sussex*, L. Lorch*, B. Schölkopf, A. Krause In Proc. International Conference on Learning Representations (ICLR), 2025 [bibtex] [abstract] [pdf]
2024
Generative Intervention Models for Causal Perturbation Modeling N. Schneider, L. Lorch, N. Kilbertus, B. Schölkopf, A. Krause In arXiv preprint arXiv:2411.14003, 2024 [bibtex] [abstract] [pdf]
Causal Modeling with Stationary Diffusions L. Lorch, A. Krause, B. Schölkopf In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), 2024 Oral presentation [bibtex] [abstract] [pdf] [code]
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] [code]
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 Spotlight presentation [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 Spotlight presentation [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 , volume 33, 2020 Spotlight presentation [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]