Lars Lorch PhD Student lars.lorch@inf.ethz.ch OAT Y 17 LinkedIn Publications 2025 Generative Intervention Models for Causal Perturbation Modeling N. Schneider, L. Lorch, N. Kilbertus, B. Schölkopf, A. KrauseIn Proc. International Conference on Machine Learning (ICML), 2025[bibtex] [abstract] [pdf] Standardizing Structural Causal Models W. Ormaniec*, S. Sussex*, L. Lorch*, B. Schölkopf, A. KrauseIn Proc. International Conference on Learning Representations (ICLR), 2025[bibtex] [abstract] [pdf] 2024 Causal Modeling with Stationary Diffusions L. Lorch, A. Krause, B. SchölkopfIn Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), 2024Oral 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. KrauseIn Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), 2023Oral presentation[bibtex] [abstract] [pdf] 2022 Amortized Inference for Causal Structure Learning L. Lorch, S. Sussex, J. Rothfuss, A. Krause, B. SchölkopfIn 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 KügelgenIn Proc. Neural Information Processing Systems (NeurIPS), 2022Spotlight presentation[bibtex] [abstract] [pdf] 2021 DiBS: Differentiable Bayesian Structure Learning L. Lorch, J. Rothfuss, B. Schölkopf, A. KrauseIn Proc. Neural Information Processing Systems (NeurIPS), 2021Spotlight 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-VelezIn , volume 33, 2020Spotlight presentation[bibtex] [abstract] [pdf]
