Niki Kilbertus

CoRR, 2024

Causal machine learning for predicting treatment outcomes.

[BibT_eX]

[DOI]

Mihaela van der Schaar

CoRR, 2024

Towards Physically Consistent Deep Learning For Climate Model Parameterizations.

[BibT_eX]

[DOI]

Birgit Kühbacher

Fernando Iglesias-Suarez

Sandeep Suresh Cranganore

Veronika Eyring

CoRR, 2024

Targeted Sequential Indirect Experiment Design.

[BibT_eX]

[DOI]

CoRR, 2024

Whole Genome Transformer for Gene Interaction Effects in Microbiome Habitat Specificity.

[BibT_eX]

[DOI]

Zhufeng Li

Nicholas D. Youngblut

CoRR, 2024

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes.

[BibT_eX]

[DOI]

Georg Manten

Cecilia Casolo

Emilio Ferrucci

Søren Wengel Mogensen

Cristopher Salvi

CoRR, 2024

ODEFormer: Symbolic Regression of Dynamical Systems with Transformers.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Supervised learning and model analysis with compositional data.

[BibT_eX]

[DOI]

PLoS Comput. Biol., 2023

Stabilized Neural Differential Equations for Learning Constrained Dynamics.

[BibT_eX]

[DOI]

CoRR, 2023

Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Predicting Ordinary Differential Equations with Transformers.

[BibT_eX]

[DOI]

Sören Becker

Michal Klein

Alexander Neitz

Proceedings of the International Conference on Machine Learning, 2023

Sequential Underspecified Instrument Selection for Cause-Effect Estimation.

[BibT_eX]

[DOI]

Elisabeth Ailer

Jason S. Hartford

Proceedings of the International Conference on Machine Learning, 2023

Modeling content creator incentives on algorithm-curated platforms.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Stochastic Causal Programming for Bounding Treatment Effects.

[BibT_eX]

[DOI]

Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022

Discovering ordinary differential equations that govern time-series.

[BibT_eX]

[DOI]

Sören Becker

Michal Klein

Alexander Neitz

CoRR, 2022

Predicting single-cell perturbation responses for unseen drugs.

[BibT_eX]

[DOI]

CoRR, 2022

Numerical Analysis of the Causal Action Principle in Low Dimensions.

[BibT_eX]

[DOI]

Felix Finster

Robert H. Jonsson

CoRR, 2022

Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sparsity in Continuous-Depth Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Multi-disciplinary fairness considerations in machine learning for clinical trials.

[BibT_eX]

[DOI]

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

2021

Beyond Predictions in Neural ODEs: Identification and Interventions.

[BibT_eX]

[DOI]

Hananeh Aliee

Fabian J. Theis

CoRR, 2021

A causal view on compositional data.

[BibT_eX]

[DOI]

Elisabeth Ailer

Christian L. Müller

CoRR, 2021

Beyond traditional assumptions in fair machine learning.

[BibT_eX]

[DOI]

CoRR, 2021

On Component Interactions in Two-Stage Recommender Systems.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On Disentangled Representations Learned from Correlated Data.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Beyond traditional assumptions in fair machine learning

[BibT_eX]

[DOI]

PhD thesis, 2020

Exploration in two-stage recommender systems.

[BibT_eX]

[DOI]

CoRR, 2020

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data.

[BibT_eX]

[DOI]

CoRR, 2020

A Class of Algorithms for General Instrumental Variable Models.

[BibT_eX]

[DOI]

Matt J. Kusner

Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fair Decisions Despite Imperfect Predictions.

[BibT_eX]

[DOI]

Manuel Gomez Rodriguez

Krikamol Muandet

Isabel Valera

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

2019

Convolutional neural networks: a magic bullet for gravitational-wave detection?

[BibT_eX]

[DOI]

CoRR, 2019

Improving Consequential Decision Making under Imperfect Predictions.

[BibT_eX]

[DOI]

Manuel Gomez-Rodriguez

Krikamol Muandet

Isabel Valera

CoRR, 2019

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

2018

Generalization in anti-causal learning.

[BibT_eX]

[DOI]

CoRR, 2018

Learning Independent Causal Mechanisms.

[BibT_eX]

[DOI]

Mateo Rojas-Carulla

Proceedings of the 35th International Conference on Machine Learning, 2018

Blind Justice: Fairness with Encrypted Sensitive Attributes.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Avoiding Discrimination through Causal Reasoning.

[BibT_eX]

[DOI]

Mateo Rojas-Carulla

Moritz Hardt

Dominik Janzing