| The inductive bias of quantum kernels J Kübler*, S Buchholz*, B Schölkopf Advances in Neural Information Processing Systems 34, 12661-12673, 2021 | 101 | 2021 |
| Multivariate central limit theorem in quantum dynamics S Buchholz, C Saffirio, B Schlein Journal of Statistical Physics 154 (1), 113-152, 2014 | 36 | 2014 |
| Function classes for identifiable nonlinear independent component analysis S Buchholz, M Besserve, B Schölkopf Advances in Neural Information Processing Systems 35, 16946-16961, 2022 | 31 | 2022 |
| Learning Linear Causal Representations from Interventions under General Nonlinear Mixing S Buchholz*, G Rajendran*, E Rosenfeld, B Aragam, B Schölkopf, ... NeurIPS 2023 (oral), 2023 | 26 | 2023 |
| Causal Component Analysis W Liang, A Kekić, J von Kügelgen, S Buchholz, M Besserve, L Gresele, ... NeurIPS 2023, 2023 | 20* | 2023 |
| Cauchy-born rule from microscopic models with non-convex potentials S Adams, S Buchholz, R Kotecký, S Müller arXiv preprint arXiv:1910.13564, 2019 | 13 | 2019 |
| Kernel interpolation in Sobolev spaces is not consistent in low dimensions S Buchholz Conference on Learning Theory, 3410-3440, 2022 | 12 | 2022 |
| Phase transitions for a class of gradient fields S Buchholz Probability Theory and Related Fields 179, 969-1022, 2021 | 11 | 2021 |
| Finite range decomposition for Gaussian measures with improved regularity S Buchholz Journal of Functional Analysis 275 (7), 1674-1711, 2018 | 10 | 2018 |
| AutoML two-sample test JM Kübler, V Stimper, S Buchholz, K Muandet, B Schölkopf Advances in Neural Information Processing Systems 35, 15929-15941, 2022 | 8 | 2022 |
| Probability to be positive for the membrane model in dimensions 2 and 3 S Buchholz, JD Deuschel, N Kurt, F Schweiger | 7 | 2019 |
| Flow Matching for Scalable Simulation-Based Inference M Dax*, J Wildberger*, S Buchholz*, SR Green, JH Macke, B Schölkopf NeurIPS 2023, 2023 | 6* | 2023 |
| Einführung in die partiellen Differentialgleichungen S Müller, S Buchholz | 3 | 2017 |
| Renormalisation in discrete elasticity SH Buchholz Universitäts-und Landesbibliothek Bonn, 2019 | 2 | 2019 |
| Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models G Rajendran, S Buchholz, B Aragam, B Schölkopf, P Ravikumar arXiv preprint arXiv:2402.09236, 2024 | 1 | 2024 |
| Aizenman-Wehr argument for a class of disordered gradient models S Buchholz, C Cotar arXiv preprint arXiv:2309.12799, 2023 | 1 | 2023 |
| Some Remarks on Identifiability of Independent Component Analysis in Restricted Function Classes S Buchholz Transactions on Machine Learning Research, 2023 | 1 | 2023 |
| Assaying large-scale testing models to interpret COVID-19 case numbers M Besserve, S Buchholz, B Schölkopf arXiv preprint arXiv:2012.01912, 2020 | 1 | 2020 |
| A Measure-Theoretic Axiomatisation of Causality J Park, S Buchholz, B Schölkopf, K Muandet NeurIPS 2023 (oral), 2023 | | 2023 |
| Multi-Armed Bandits and Quantum Channel Oracles S Buchholz, JM Kübler, B Schölkopf arXiv preprint arXiv:2301.08544, 2023 | | 2023 |
