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Simon Buchholz
Simon Buchholz
Max Planck Institute for Intelligent Systems
Verified email at tue.mpg.de
Title
Cited by
Cited by
Year
The inductive bias of quantum kernels
J Kübler*, S Buchholz*, B Schölkopf
Advances in Neural Information Processing Systems 34, 12661-12673, 2021
1072021
Multivariate central limit theorem in quantum dynamics
S Buchholz, C Saffirio, B Schlein
Journal of Statistical Physics 154 (1), 113-152, 2014
372014
Function classes for identifiable nonlinear independent component analysis
S Buchholz, M Besserve, B Schölkopf
Advances in Neural Information Processing Systems 35, 16946-16961, 2022
342022
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
S Buchholz*, G Rajendran*, E Rosenfeld, B Aragam, B Schölkopf, ...
NeurIPS 2023 (oral), 2023
302023
Causal Component Analysis
W Liang, A Kekić, J von Kügelgen, S Buchholz, M Besserve, L Gresele, ...
NeurIPS 2023, 2023
23*2023
Kernel interpolation in Sobolev spaces is not consistent in low dimensions
S Buchholz
Conference on Learning Theory, 3410-3440, 2022
152022
Cauchy-born rule from microscopic models with non-convex potentials
S Adams, S Buchholz, R Kotecký, S Müller
arXiv preprint arXiv:1910.13564, 2019
132019
Flow Matching for Scalable Simulation-Based Inference
M Dax*, J Wildberger*, S Buchholz*, SR Green, JH Macke, B Schölkopf
NeurIPS 2023, 2023
12*2023
Phase transitions for a class of gradient fields
S Buchholz
Probability Theory and Related Fields 179, 969-1022, 2021
122021
AutoML two-sample test
JM Kübler, V Stimper, S Buchholz, K Muandet, B Schölkopf
Advances in Neural Information Processing Systems 35, 15929-15941, 2022
102022
Finite range decomposition for Gaussian measures with improved regularity
S Buchholz
Journal of Functional Analysis 275 (7), 1674-1711, 2018
102018
Probability to be positive for the membrane model in dimensions 2 and 3
S Buchholz, JD Deuschel, N Kurt, F Schweiger
72019
Cauchy-Born Rule from Microscopic Models with Non-convex Potentials. 2019
S Adams, S Buchholz, R Kotecký, S Müller
Preprint, 0
5
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
32024
Einführung in die partiellen Differentialgleichungen
S Müller, S Buchholz
32017
Renormalisation in discrete elasticity
SH Buchholz
Universitäts-und Landesbibliothek Bonn, 2019
22019
Aizenman-Wehr argument for a class of disordered gradient models
S Buchholz, C Cotar
arXiv preprint arXiv:2309.12799, 2023
12023
Some Remarks on Identifiability of Independent Component Analysis in Restricted Function Classes
S Buchholz
Transactions on Machine Learning Research, 2023
12023
A Measure-Theoretic Axiomatisation of Causality
J Park, S Buchholz, B Schölkopf, K Muandet
NeurIPS 2023 (oral), 2023
12023
Assaying large-scale testing models to interpret COVID-19 case numbers
M Besserve, S Buchholz, B Schölkopf
arXiv preprint arXiv:2012.01912, 2020
12020
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Articles 1–20