Alicia Curth - RankMe

39 papers A* 13A 2Journal 21Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	E-Scores for (In)Correctness Assessment of Generative Model Outputs. CoRR Guneet S. Dhillon, Javier González, Teodora Pandeva, Alicia Curth
2024	A*	`conf`	A Neural Framework for Generalized Causal Sensitivity Analysis. ICLR Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
2024	J	`jnl`	Causal machine learning for predicting treatment outcomes. CoRR Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess, Alicia Curth, Stefan Bauer, Niki Kilbertus, Isaac S. Kohane, Mihaela van der Schaar
2024	—	`conf`	Cautionary Tales on Synthetic Controls in Survival Analyses. CLeaR Alicia Curth, Hoifung Poon, Aditya V. Nori, Javier González
2024	J	`jnl`	Classical Statistical (In-Sample) Intuitions Don't Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random Designs. CoRR Alicia Curth
2024	A*	`conf`	Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond. NeurIPS Alan Jeffares, Alicia Curth, Mihaela van der Schaar
2024	J	`jnl`	Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond. CoRR Alan Jeffares, Alicia Curth, Mihaela van der Schaar
2024	A*	`conf`	Defining Expertise: Applications to Treatment Effect Estimation. ICLR Alihan Hüyük, Qiyao Wei, Alicia Curth, Mihaela van der Schaar
2024	J	`jnl`	Defining Expertise: Applications to Treatment Effect Estimation. CoRR Alihan Hüyük, Qiyao Wei, Alicia Curth, Mihaela van der Schaar
2024	J	`jnl`	Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers. CoRR Alicia Curth, Alan Jeffares, Mihaela van der Schaar
2023	J	`jnl`	A Neural Framework for Generalized Causal Sensitivity Analysis. CoRR Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
2023	A*	`conf`	A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning. NeurIPS Alicia Curth, Alan Jeffares, Mihaela van der Schaar
2023	J	`jnl`	A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning. CoRR Alicia Curth, Alan Jeffares, Mihaela van der Schaar
2023	A*	`conf`	Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. ICML Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar
2023	J	`jnl`	Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. CoRR Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar
2023	A*	`conf`	Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions. ICML Alicia Curth, Alihan Hüyük, Mihaela van der Schaar
2023	A*	`conf`	In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. ICML Alicia Curth, Mihaela van der Schaar
2023	J	`jnl`	In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. CoRR Alicia Curth, Mihaela van der Schaar
2023	A	`conf`	Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. AISTATS Alicia Curth, Mihaela van der Schaar
2023	J	`jnl`	Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. CoRR Alicia Curth, Mihaela van der Schaar
2022	J	`jnl`	Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials. CoRR Alicia Curth, Alihan Hüyük, Mihaela van der Schaar
2022	A*	`conf`	Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. NeurIPS Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar
2022	J	`jnl`	Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. CoRR Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar
2022	J	`jnl`	Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects. CoRR Tobias Hatt, Jeroen Berrevoets, Alicia Curth, Stefan Feuerriegel, Mihaela van der Schaar
2022	A*	`conf`	HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. ICML Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar
2022	J	`jnl`	HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. CoRR Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar
2022	A*	`conf`	Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies. ICLR Alex J. Chan, Alicia Curth, Mihaela van der Schaar
2022	J	`jnl`	Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies. CoRR Alex J. Chan, Alicia Curth, Mihaela van der Schaar
2021	J	`jnl`	Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time. CoRR Jeroen Berrevoets, Alicia Curth, Ioana Bica, Eoin F. McKinney, Mihaela van der Schaar
2021	J	`jnl`	Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators. CoRR Alicia Curth, Mihaela van der Schaar
2021	A*	`conf`	Estimating Multi-cause Treatment Effects via Single-cause Perturbation. NeurIPS Zhaozhi Qian, Alicia Curth, Mihaela van der Schaar
2021	A	`conf`	Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. AISTATS Alicia Curth, Mihaela van der Schaar
2021	J	`jnl`	Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. CoRR Alicia Curth, Mihaela van der Schaar
2021	A*	`conf`	On Inductive Biases for Heterogeneous Treatment Effect Estimation. NeurIPS Alicia Curth, Mihaela van der Schaar
2021	J	`jnl`	On Inductive Biases for Heterogeneous Treatment Effect Estimation. CoRR Alicia Curth, Mihaela van der Schaar
2021	—	`conf`	Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation. NeurIPS Datasets and Benchmarks Alicia Curth, David Svensson, James Weatherall, Mihaela van der Schaar
2021	A*	`conf`	SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data. NeurIPS Alicia Curth, Changhee Lee, Mihaela van der Schaar
2021	J	`jnl`	SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data. CoRR Alicia Curth, Changhee Lee, Mihaela van der Schaar
2019	—	`conf`	Transferring Clinical Prediction Models Across Hospitals and Electronic Health Record Systems. PKDD/ECML Workshops (1) Alicia Curth, Patrick Thoral, Wilco van den Wildenberg, Peter Bijlstra, Daan P. de Bruin, Paul W. G. Elbers, Mattia Fornasa