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Mark J. van der Laan
- > Home > Persons > Mark J. van der Laan
Publications
- 2023
- [j20]Philippe Boileau, Nima S. Hejazi, Mark J. van der Laan, Sandrine Dudoit:
Cross-Validated Loss-based Covariance Matrix Estimator Selection in High Dimensions. J. Comput. Graph. Stat. 32(2): 601-612 (2023) - [j19]David McCoy, Alan E. Hubbard, Mark J. van der Laan:
CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R. J. Open Source Softw. 8(82): 4181 (2023) - [j18]David McCoy, Alejandro Schuler, Alan E. Hubbard, Mark J. van der Laan:
SuperNOVA: Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions in R. J. Open Source Softw. 8(91): 5422 (2023) - [i16]Ivana Malenica, Rachael V. Phillips, Daniel Lazzareschi, Jeremy R. Coyle, Romain Pirracchio, Mark J. van der Laan:
Multi-task Highly Adaptive Lasso. CoRR abs/2301.12029 (2023) - 2022
- [j17]Nima S. Hejazi, Mark J. van der Laan, David C. Benkeser:
'haldensify': Highly adaptive lasso conditional density estimation in 'R'. J. Open Source Softw. 7(78): 4522 (2022) - [j16]Gilmer Valdes, Yannet Interian, Efstathios D. Gennatas, Mark J. van der Laan:
The Conditional Super Learner. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 10236-10243 (2022) - [i14]Alejandro Schuler, Mark J. van der Laan:
The Selectively Adaptive Lasso. CoRR abs/2205.10697 (2022) - 2021
- [j15]Philippe Boileau, Nima S. Hejazi, Brian Collica, Mark J. van der Laan, Sandrine Dudoit:
cvCovEst: Cross-validated covariance matrix estimator selection and evaluation in R. J. Open Source Softw. 6(63): 3273 (2021) - [c7]Aurélien Bibaut, Nathan Kallus, Maria Dimakopoulou, Antoine Chambaz, Mark J. van der Laan:
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. NeurIPS 2021: 19261-19273 - [c6]Aurélien Bibaut, Maria Dimakopoulou, Nathan Kallus, Antoine Chambaz, Mark J. van der Laan:
Post-Contextual-Bandit Inference. NeurIPS 2021: 28548-28559 - [i13]Ivana Malenica, Aurélien Bibaut, Mark J. van der Laan:
Adaptive Sequential Design for a Single Time-Series. CoRR abs/2102.00102 (2021) - [i12]Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark J. van der Laan:
Post-Contextual-Bandit Inference. CoRR abs/2106.00418 (2021) - [i11]Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark J. van der Laan:
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. CoRR abs/2106.01723 (2021) - [i10]Ivana Malenica, Rachael V. Phillips, Romain Pirracchio, Antoine Chambaz, Alan E. Hubbard, Mark J. van der Laan:
Personalized Online Machine Learning. CoRR abs/2109.10452 (2021) - 2020
- [j14]Nima S. Hejazi, Jeremy R. Coyle, Mark J. van der Laan:
hal9001: Scalable highly adaptive lasso regression in R. J. Open Source Softw. 5(53): 2526 (2020) - [c5]Aurélien Bibaut, Antoine Chambaz, Mark J. van der Laan:
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits. UAI 2020: 1099-1108 - [i8]Aurélien F. Bibaut, Antoine Chambaz, Mark J. van der Laan:
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits. CoRR abs/2003.02873 (2020) - [i7]Aurélien F. Bibaut, Antoine Chambaz, Mark J. van der Laan:
Rate-adaptive model selection over a collection of black-box contextual bandit algorithms. CoRR abs/2006.03632 (2020) - 2019
- [c4]Aurélien Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan:
More Efficient Off-Policy Evaluation through Regularized Targeted Learning. ICML 2019: 654-663 - [i6]Efstathios D. Gennatas, Jerome H. Friedman, Lyle H. Ungar, Romain Pirracchio, Eric Eaton, L. Reichman, Yannet Interian, Charles B. Simone II, A. Auerbach, E. Delgado, Mark J. van der Laan, Timothy D. Solberg, Gilmer Valdes:
Expert-Augmented Machine Learning. CoRR abs/1903.09731 (2019) - [i5]Aurélien F. Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan:
More Efficient Off-Policy Evaluation through Regularized Targeted Learning. CoRR abs/1912.06292 (2019) - [i4]Gilmer Valdes, Yannet Interian, Efstathios D. Gennatas, Mark J. van der Laan:
Conditional Super Learner. CoRR abs/1912.06675 (2019) - 2018
- [i3]Mark J. van der Laan, Ivana Malenica:
Robust Estimation of Data-Dependent Causal Effects based on Observing a Single Time-Series. CoRR abs/1809.00734 (2018) - [i2]Nima S. Hejazi, Rachael V. Phillips, Alan E. Hubbard, Mark J. van der Laan:
methyvim: Targeted, robust, and model-free differential methylation analysis in R. F1000Research 7: 1424 (2018) - 2017
- [i1]Cheng Ju, Aurélien Bibaut, Mark J. van der Laan:
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification. CoRR abs/1704.01664 (2017) - 2016
- [c3]David C. Benkeser, Mark J. van der Laan:
The Highly Adaptive Lasso Estimator. DSAA 2016: 689-696 - [p3]Alan E. Hubbard, Mark J. van der Laan:
Mining with Inference: Data-Adaptive Target Parameters. Handbook of Big Data 2016: 439-452 - 2010
- [j12]Annette M. Molinaro, Karen Lostritto, Mark J. van der Laan:
partDSA: deletion/substitution/addition algorithm for partitioning the covariate space in prediction. Bioinform. 26(10): 1357-1363 (2010) - 2006
- [j7]Sündüz Keles, Mark J. van der Laan, Sandrine Dudoit, Simon E. Cawley:
Multiple Testing Methods For ChIP - Chip High Density Oligonucleotide Array Data. J. Comput. Biol. 13(3): 579-613 (2006) - 2004
- [j4]Sündüz Keles, Mark J. van der Laan, Chris Vulpe:
Regulatory motif finding by logic regression. Bioinform. 20(16): 2799-2811 (2004) - 2003
- [j2]Sandrine Dudoit, Mark J. van der Laan, Sündüz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng:
Loss-based estimation with cross-validation: applications to microarray data analysis. SIGKDD Explor. 5(2): 56-68 (2003) - [p2]Sündüz Keles, Mark J. van der Laan, James M. Robins:
Estimation of the Bivariate Survival Function with Generalized Bivariate Right Censored Data Structures. Advances in Survival Analysis 2003: 143-173 - 2002
- [j1]Sündüz Keles, Mark J. van der Laan, Michael B. Eisen:
Identification of regulatory elements using a feature selection method. Bioinform. 18(9): 1167-1175 (2002)
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