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ORCIDAlicia Curth
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2020 – today
- 2024
[c18]Alicia Curth, Hoifung Poon, Aditya V. Nori, Javier González:
Cautionary Tales on Synthetic Controls in Survival Analyses. CLeaR 2024: 143-159- [c17]Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar:
A Neural Framework for Generalized Causal Sensitivity Analysis. ICLR 2024 - [c16]Alihan Hüyük, Qiyao Wei, Alicia Curth, Mihaela van der Schaar:
Defining Expertise: Applications to Treatment Effect Estimation. ICLR 2024 - [c15]Alan Jeffares, Alicia Curth, Mihaela van der Schaar:
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond. NeurIPS 2024 - [i20]Alicia Curth, Alan Jeffares, Mihaela van der Schaar:
Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers. CoRR abs/2402.01502 (2024) - [i19]Alihan Hüyük, Qiyao Wei, Alicia Curth, Mihaela van der Schaar:
Defining Expertise: Applications to Treatment Effect Estimation. CoRR abs/2403.00694 (2024) - [i18]Alicia Curth:
Classical Statistical (In-Sample) Intuitions Don't Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random Designs. CoRR abs/2409.18842 (2024) - [i17]Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess, Alicia Curth, Stefan Bauer, Niki Kilbertus, Isaac S. Kohane, Mihaela van der Schaar:
Causal machine learning for predicting treatment outcomes. CoRR abs/2410.08770 (2024) - [i16]Alan Jeffares, Alicia Curth, Mihaela van der Schaar:
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond. CoRR abs/2411.00247 (2024) - 2023
- [c14]Alicia Curth, Mihaela van der Schaar:
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. AISTATS 2023: 7961-7980 - [c13]Alicia Curth, Alihan Hüyük, Mihaela van der Schaar:
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions. ICML 2023: 6603-6622 - [c12]Alicia Curth, Mihaela van der Schaar:
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. ICML 2023: 6623-6642 - [c11]Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar:
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. ICML 2023: 34855-34874 - [c10]Alicia Curth, Alan Jeffares, Mihaela van der Schaar:
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning. NeurIPS 2023 - [i15]Alicia Curth, Mihaela van der Schaar:
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. CoRR abs/2302.02923 (2023) - [i14]Alicia Curth, Mihaela van der Schaar:
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. CoRR abs/2302.12718 (2023) - [i13]Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar:
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. CoRR abs/2306.04255 (2023) - [i12]Alicia Curth, Alan Jeffares, Mihaela van der Schaar:
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning. CoRR abs/2310.18988 (2023) - [i11]Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar:
A Neural Framework for Generalized Causal Sensitivity Analysis. CoRR abs/2311.16026 (2023) - 2022
- [c9]Alex J. Chan, Alicia Curth, Mihaela van der Schaar:
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies. ICLR 2022 - [c8]Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar:
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. ICML 2022: 9916-9937 - [c7]Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar:
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. NeurIPS 2022 - [i10]Tobias Hatt, Jeroen Berrevoets, Alicia Curth, Stefan Feuerriegel, Mihaela van der Schaar:
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects. CoRR abs/2202.12891 (2022) - [i9]Alex J. Chan, Alicia Curth, Mihaela van der Schaar:
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies. CoRR abs/2203.07338 (2022) - [i8]Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar:
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. CoRR abs/2206.07769 (2022) - [i7]Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar:
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. CoRR abs/2206.08363 (2022) - [i6]Alicia Curth, Alihan Hüyük, Mihaela van der Schaar:
Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials. CoRR abs/2208.05844 (2022) - 2021
- [c6]Alicia Curth, Mihaela van der Schaar:
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. AISTATS 2021: 1810-1818 - [c5]Alicia Curth, Mihaela van der Schaar:
On Inductive Biases for Heterogeneous Treatment Effect Estimation. NeurIPS 2021: 15883-15894 - [c4]Alicia Curth, Changhee Lee, Mihaela van der Schaar:
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data. NeurIPS 2021: 26740-26753 - [c3]Alicia Curth, David Svensson, James Weatherall, Mihaela van der Schaar:
Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation. NeurIPS Datasets and Benchmarks 2021 - [c2]Zhaozhi Qian, Alicia Curth, Mihaela van der Schaar:
Estimating Multi-cause Treatment Effects via Single-cause Perturbation. NeurIPS 2021: 23754-23767 - [i5]Alicia Curth, Mihaela van der Schaar:
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. CoRR abs/2101.10943 (2021) - [i4]Alicia Curth, Mihaela van der Schaar:
On Inductive Biases for Heterogeneous Treatment Effect Estimation. CoRR abs/2106.03765 (2021) - [i3]Alicia Curth, Mihaela van der Schaar:
Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators. CoRR abs/2107.13346 (2021) - [i2]Alicia Curth, Changhee Lee, Mihaela van der Schaar:
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data. CoRR abs/2110.14001 (2021) - [i1]Jeroen Berrevoets, Alicia Curth, Ioana Bica, Eoin F. McKinney, Mihaela van der Schaar:
Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time. CoRR abs/2112.03811 (2021)
2010 – 2019
- 2019
- [c1]Alicia Curth, Patrick Thoral, Wilco van den Wildenberg, Peter Bijlstra, Daan P. de Bruin, Paul W. G. Elbers, Mattia Fornasa:
Transferring Clinical Prediction Models Across Hospitals and Electronic Health Record Systems. PKDD/ECML Workshops (1) 2019: 605-621
Coauthor Index
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