Trustless Intermediation in Blockchain-Based Decentralized Service Marketplaces. - Markus Klems; Jacob Eberhardt; Stefan Tai; Steffen Härtlein; Simon Buchholz; Ahmed Tidjani (ICSOC, 2017)

Using application knowledge to reduce cold starts in FaaS services. - David Bermbach; Ahmet-Serdar Karakaya; Simon Buchholz (SAC, 2020)

The Inductive Bias of Quantum Kernels. - Jonas M. Kübler; Simon Buchholz; Bernhard Schölkopf (NeurIPS, 2021)

Kernel interpolation in Sobolev spaces is not consistent in low dimensions. - Simon Buchholz (COLT, 2022)

Function Classes for Identifiable Nonlinear Independent Component Analysis. - Simon Buchholz; Michel Besserve; Bernhard Schölkopf (NeurIPS, 2022)

AutoML Two-Sample Test. - Jonas M. Kübler; Vincent Stimper; Simon Buchholz; Krikamol Muandet; Bernhard Schölkopf (NeurIPS, 2022)

Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. - Simon Buchholz; Goutham Rajendran; Elan Rosenfeld; Bryon Aragam; Bernhard Schölkopf; Pradeep Ravikumar (NeurIPS, 2023)

Causal Component Analysis. - Wendong Liang; Armin Kekic; Julius von Kügelgen; Simon Buchholz; Michel Besserve; Luigi Gresele; Bernhard Schölkopf (NeurIPS, 2023)

A Measure-Theoretic Axiomatisation of Causality. - Junhyung Park; Simon Buchholz; Bernhard Schölkopf; Krikamol Muandet (NeurIPS, 2023)

Flow Matching for Scalable Simulation-Based Inference. - Jonas Wildberger; Maximilian Dax; Simon Buchholz; Stephen R. Green; Jakob H. Macke; Bernhard Schölkopf (NeurIPS, 2023)

Some Remarks on Identifiability of Independent Component Analysis in Restricted Function Classes. - Simon Buchholz (Trans. Mach. Learn. Res., 2023)

Robustness of Nonlinear Representation Learning. - Simon Buchholz; Bernhard Schölkopf (ICML, 2024)

Learning Partitions from Context. - Simon Buchholz (NeurIPS, 2024)

From Causal to Concept-Based Representation Learning. - Goutham Rajendran; Simon Buchholz; Bryon Aragam; Bernhard Schölkopf; Pradeep Ravikumar (NeurIPS, 2024)

Products, Abstractions and Inclusions of Causal Spaces. - Simon Buchholz; Junhyung Park; Bernhard Schölkopf (UAI, 2024)

Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models. - Goutham Rajendran; Simon Buchholz; Bryon Aragam; Bernhard Schölkopf; Pradeep Ravikumar (CoRR, 2024)

Algorithmic causal structure emerging through compression. - Wendong Liang; Simon Buchholz; Bernhard Schölkopf (CLeaR, 2025)

Interaction Asymmetry: A General Principle for Learning Composable Abstractions. - Jack Brady; Julius von Kügelgen; Sébastien Lachapelle; Simon Buchholz; Thomas Kipf; Wieland Brendel (ICLR, 2025)

Multi-Armed Bandits and Quantum Channel Oracles. - Simon Buchholz; Jonas M. Kübler; Bernhard Schölkopf (Quantum, 2025)

Reparameterized LLM Training via Orthogonal Equivalence Transformation. - Zeju Qiu; Simon Buchholz; Tim Z. Xiao; Maximilian Dax; Bernhard Schölkopf; Weiyang Liu (CoRR, 2025)