Patrick Blöbaum

According to our database, Patrick Blöbaum authored at least 31 papers between 2015 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

A Classical View on Benign Overfitting: The Role of Sample Size.

Junhyung Park, Patrick Blöbaum, Shiva Prasad Kasiviswanathan

CoRR, May, 2025

Cross-validating causal discovery via Leave-One-Variable-Out.

Daniela Schkoda, Philipp Michael Faller, Dominik Janzing, Patrick Blöbaum

Proceedings of the Causal Learning and Reasoning, Lausanne, Switzerland, 7-9 May 2025., 2025

Beyond Single-Feature Importance with ICECREAM.

Michael Oesterle, Patrick Blöbaum, Atalanti-Anastasia Mastakouri, Elke Kirschbaum

Proceedings of the Causal Learning and Reasoning, Lausanne, Switzerland, 7-9 May 2025., 2025

Score matching through the roof: linear, nonlinear, and latent variables causal discovery.

Francesco Montagna, Philipp Michael Faller, Patrick Blöbaum, Elke Kirschbaum, Francesco Locatello

Proceedings of the Causal Learning and Reasoning, Lausanne, Switzerland, 7-9 May 2025., 2025

Toward Falsifying Causal Graphs Using a Permutation-Based Test.

Elias Eulig, Atalanti-Anastasia Mastakouri, Patrick Blöbaum, Michaela Hardt, Dominik Janzing

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries.

Patrick Chao, Patrick Blöbaum, Sapan Kirit Patel, Shiva Prasad Kasiviswanathan

Trans. Mach. Learn. Res., 2024

DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models.

Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti-Anastasia Mastakouri, Dominik Janzing

J. Mach. Learn. Res., 2024

Benign Overfitting for Regression with Trained Two-Layer ReLU Networks.

Junhyung Park, Patrick Blöbaum, Shiva Prasad Kasiviswanathan

CoRR, 2024

Root Cause Analysis of Outliers with Missing Structural Knowledge.

Nastaran Okati, Sergio Hernan Garrido Mejia, William Roy Orchard, Patrick Blöbaum, Dominik Janzing

CoRR, 2024

Causal vs. Anticausal merging of predictors.

Sergio Hernan Garrido Mejia, Patrick Blöbaum, Bernhard Schölkopf, Dominik Janzing

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

The PetShop Dataset - Finding Causes of Performance Issues across Microservices.

Michaela Hardt, William Roy Orchard, Patrick Blöbaum, Elke Kirschbaum, Shiva Prasad Kasiviswanathan

Proceedings of the Causal Learning and Reasoning, 2024

Quantifying intrinsic causal contributions via structure preserving interventions.

Dominik Janzing, Patrick Blöbaum, Atalanti-Anastasia Mastakouri, Philipp Michael Faller, Lenon Minorics, Kailash Budhathoki

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Interventional and Counterfactual Inference with Diffusion Models.

Patrick Chao, Patrick Blöbaum, Shiva Prasad Kasiviswanathan

CoRR, 2023

Sequential Kernelized Independence Testing.

Aleksandr Podkopaev, Patrick Blöbaum, Shiva Prasad Kasiviswanathan, Aaditya Ramdas

Proceedings of the International Conference on Machine Learning, 2023

Thompson Sampling with Diffusion Generative Prior.

Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton, Patrick Blöbaum

Proceedings of the International Conference on Machine Learning, 2023

Manifold Restricted Interventional Shapley Values.

Muhammad Faaiz Taufiq, Patrick Blöbaum, Lenon Minorics

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

On Measuring Causal Contributions via do-interventions.

Yonghan Jung, Shiva Prasad Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim

Proceedings of the International Conference on Machine Learning, 2022

Causal structure-based root cause analysis of outliers.

Kailash Budhathoki, Lenon Minorics, Patrick Blöbaum, Dominik Janzing

Proceedings of the International Conference on Machine Learning, 2022

Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies.

Lenon Minorics, Ali Caner Türkmen, David Kernert, Patrick Blöbaum, Laurent Callot, Dominik Janzing

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Why did the distribution change?

Kailash Budhathoki, Dominik Janzing, Patrick Blöbaum, Hoiyi Ng

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Quantifying causal contribution via structure preserving interventions.

Dominik Janzing, Patrick Blöbaum, Lenon Minorics

CoRR, 2020

Feature relevance quantification in explainable AI: A causal problem.

Dominik Janzing, Lenon Minorics, Patrick Blöbaum

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Analysis of cause-effect inference by comparing regression errors.

Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf

PeerJ Comput. Sci., 2019

Feature relevance quantification in explainable AI: A causality problem.

Dominik Janzing, Lenon Minorics, Patrick Blöbaum

CoRR, 2019

2018

Analysis of Cause-Effect Inference via Regression Errors.

Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf

CoRR, 2018

Cause-Effect Inference by Comparing Regression Errors.

Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Estimation of interventional effects of features on prediction.

Patrick Blöbaum, Shohei Shimizu

Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, 2017

A novel principle for causal inference in data with small error variance.

Patrick Blöbaum, Shohei Shimizu, Takashi Washio

Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

2016

Error Asymmetry in Causal and Anticausal Regression.

Patrick Blöbaum, Takashi Washio, Shohei Shimizu

CoRR, 2016

2015

Discriminative and Generative Models in Causal and Anticausal Settings.

Patrick Blöbaum, Shohei Shimizu, Takashi Washio

Proceedings of the Advanced Methodologies for Bayesian Networks, 2015

Unsupervised Dimensionality Reduction for Transfer Learning.

Patrick Blöbaum, Alexander Schulz, Barbara Hammer

Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015