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Journal Articles
- 2023
[j1]Simon Buchholz:
Some Remarks on Identifiability of Independent Component Analysis in Restricted Function Classes. Trans. Mach. Learn. Res. 2023 (2023)
Conference and Workshop Papers
- 2024
- [c11]Simon Buchholz, Bernhard Schölkopf:
Robustness of Nonlinear Representation Learning. ICML 2024 - 2023
- [c10]Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. NeurIPS 2023 - [c9]Wendong Liang, Armin Kekic, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf:
Causal Component Analysis. NeurIPS 2023 - [c8]Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet:
A Measure-Theoretic Axiomatisation of Causality. NeurIPS 2023 - [c7]Jonas Wildberger, Maximilian Dax, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf:
Flow Matching for Scalable Simulation-Based Inference. NeurIPS 2023 - 2022
- [c6]Simon Buchholz:
Kernel interpolation in Sobolev spaces is not consistent in low dimensions. COLT 2022: 3410-3440 - [c5]Simon Buchholz, Michel Besserve, Bernhard Schölkopf:
Function Classes for Identifiable Nonlinear Independent Component Analysis. NeurIPS 2022 - [c4]Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf:
AutoML Two-Sample Test. NeurIPS 2022 - 2021
- [c3]Jonas M. Kübler, Simon Buchholz, Bernhard Schölkopf:
The Inductive Bias of Quantum Kernels. NeurIPS 2021: 12661-12673 - 2020
- [c2]David Bermbach, Ahmet-Serdar Karakaya, Simon Buchholz:
Using application knowledge to reduce cold starts in FaaS services. SAC 2020: 134-143 - 2017
- [c1]Markus Klems, Jacob Eberhardt, Stefan Tai, Steffen Härtlein, Simon Buchholz, Ahmed Tidjani:
Trustless Intermediation in Blockchain-Based Decentralized Service Marketplaces. ICSOC 2017: 731-739
Informal and Other Publications
- 2024
- [i7]Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models. CoRR abs/2402.09236 (2024) - 2023
- [i6]Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet
:
A Measure-Theoretic Axiomatisation of Causality. CoRR abs/2305.17139 (2023) - [i5]Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf:
Flow Matching for Scalable Simulation-Based Inference. CoRR abs/2305.17161 (2023) - [i4]Wendong Liang, Armin Kekic, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf:
Causal Component Analysis. CoRR abs/2305.17225 (2023) - [i3]Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. CoRR abs/2306.02235 (2023) - 2022
- [i2]Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf:
AutoML Two-Sample Test. CoRR abs/2206.08843 (2022) - [i1]Simon Buchholz, Michel Besserve, Bernhard Schölkopf:
Function Classes for Identifiable Nonlinear Independent Component Analysis. CoRR abs/2208.06406 (2022)
Coauthor Index
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