Alicia Curth

According to our database, Alicia Curth authored at least 23 papers between 2019 and 2024.

Collaborative distances:

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

2024

Classical Statistical (In-Sample) Intuitions Don't Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random Designs.

Alicia Curth

CoRR, 2024

Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers.

Alicia Curth, Alan Jeffares, Mihaela van der Schaar

CoRR, 2024

Defining Expertise: Applications to Treatment Effect Estimation.

Alihan Hüyük, Qiyao Wei, Alicia Curth, Mihaela van der Schaar

Proceedings of the Twelfth International Conference on Learning Representations, 2024

A Neural Framework for Generalized Causal Sensitivity Analysis.

Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Cautionary Tales on Synthetic Controls in Survival Analyses.

Alicia Curth, Hoifung Poon, Aditya V. Nori, Javier González

Proceedings of the Causal Learning and Reasoning, 2024

2023

A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning.

Alicia Curth, Alan Jeffares, Mihaela van der Schaar

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

Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time.

Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar

Proceedings of the International Conference on Machine Learning, 2023

In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation.

Alicia Curth, Mihaela van der Schaar

Proceedings of the International Conference on Machine Learning, 2023

Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions.

Alicia Curth, Alihan Hüyük, Mihaela van der Schaar

Proceedings of the International Conference on Machine Learning, 2023

Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data.

Alicia Curth, Mihaela van der Schaar

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

2022

Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials.

Alicia Curth, Alihan Hüyük, Mihaela van der Schaar

CoRR, 2022

Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects.

Tobias Hatt, Jeroen Berrevoets, Alicia Curth, Stefan Feuerriegel, Mihaela van der Schaar

CoRR, 2022

Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability.

Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar

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

HyperImpute: Generalized Iterative Imputation with Automatic Model Selection.

Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar

Proceedings of the International Conference on Machine Learning, 2022

Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies.

Alex J. Chan, Alicia Curth, Mihaela van der Schaar

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time.

Jeroen Berrevoets, Alicia Curth, Ioana Bica, Eoin F. McKinney, Mihaela van der Schaar

CoRR, 2021

Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators.

Alicia Curth, Mihaela van der Schaar

CoRR, 2021

Estimating Multi-cause Treatment Effects via Single-cause Perturbation.

Zhaozhi Qian, Alicia Curth, Mihaela van der Schaar

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

Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation.

Alicia Curth, David Svensson, James Weatherall, Mihaela van der Schaar

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

On Inductive Biases for Heterogeneous Treatment Effect Estimation.

Alicia Curth, Mihaela van der Schaar

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

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data.

Alicia Curth, Changhee Lee, Mihaela van der Schaar

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

Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms.

Alicia Curth, Mihaela van der Schaar

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

2019

Transferring Clinical Prediction Models Across Hospitals and Electronic Health Record Systems.

Alicia Curth, Patrick Thoral, Wilco van den Wildenberg, Peter Bijlstra, Daan P. de Bruin, Paul W. G. Elbers, Mattia Fornasa

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019