Mark J. van der Laan

According to our database, Mark J. van der Laan authored at least 43 papers between 2002 and 2023.

Collaborative distances:

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

2023

SuperNOVA: Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions in R.

David McCoy, Alejandro Schuler, Alan E. Hubbard, Mark J. van der Laan

J. Open Source Softw., November, 2023

Cross-Validated Loss-based Covariance Matrix Estimator Selection in High Dimensions.

Philippe Boileau, Nima S. Hejazi, Mark J. van der Laan, Sandrine Dudoit

J. Comput. Graph. Stat., April, 2023

CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R.

David McCoy, Alan E. Hubbard, Mark J. van der Laan

J. Open Source Softw., February, 2023

Conformal Meta-learners for Predictive Inference of Individual Treatment Effects.

Ahmed Alaa, Zaid Ahmad, Mark J. van der Laan

CoRR, 2023

Multi-task Highly Adaptive Lasso.

Ivana Malenica, Rachael V. Phillips, Daniel Lazzareschi, Jeremy R. Coyle, Romain Pirracchio, Mark J. van der Laan

CoRR, 2023

Conformal Meta-learners for Predictive Inference of Individual Treatment Effects.

Ahmed M. Alaa, Zaid Ahmad, Mark J. van der Laan

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

2022

The Conditional Super Learner.

Gilmer Valdes, Yannet Interian, Efstathios D. Gennatas, Mark J. van der Laan

IEEE Trans. Pattern Anal. Mach. Intell., 2022

'haldensify': Highly adaptive lasso conditional density estimation in 'R'.

Nima S. Hejazi, Mark J. van der Laan, David C. Benkeser

J. Open Source Softw., 2022

The Selectively Adaptive Lasso.

Alejandro Schuler, Mark J. van der Laan

CoRR, 2022

2021

cvCovEst: Cross-validated covariance matrix estimator selection and evaluation in R.

Philippe Boileau, Nima S. Hejazi, Brian Collica, Mark J. van der Laan, Sandrine Dudoit

J. Open Source Softw., 2021

Why Machine Learning Cannot Ignore Maximum Likelihood Estimation.

Mark J. van der Laan, Sherri Rose

CoRR, 2021

Personalized Online Machine Learning.

Ivana Malenica, Rachael V. Phillips, Romain Pirracchio, Antoine Chambaz, Alan E. Hubbard, Mark J. van der Laan

CoRR, 2021

Adaptive Sequential Design for a Single Time-Series.

Ivana Malenica, Aurélien Bibaut, Mark J. van der Laan

CoRR, 2021

Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning.

Aurélien Bibaut, Nathan Kallus, Maria Dimakopoulou, Antoine Chambaz, Mark J. van der Laan

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

Post-Contextual-Bandit Inference.

Aurélien Bibaut, Maria Dimakopoulou, Nathan Kallus, Antoine Chambaz, Mark J. van der Laan

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

2020

hal9001: Scalable highly adaptive lasso regression in R.

Nima S. Hejazi, Jeremy R. Coyle, Mark J. van der Laan

J. Open Source Softw., 2020

Rate-adaptive model selection over a collection of black-box contextual bandit algorithms.

Aurélien F. Bibaut, Antoine Chambaz, Mark J. van der Laan

CoRR, 2020

Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits.

Aurélien Bibaut, Antoine Chambaz, Mark J. van der Laan

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

2019

Expert-Augmented Machine Learning.

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

CoRR, 2019

More Efficient Off-Policy Evaluation through Regularized Targeted Learning.

Aurélien Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

<i>methyvim</i>: Targeted, robust, and model-free differential methylation analysis in R.

Nima S. Hejazi, Rachael V. Phillips, Alan E. Hubbard, Mark J. van der Laan

F1000Research, 2018

Robust Estimation of Data-Dependent Causal Effects based on Observing a Single Time-Series.

Mark J. van der Laan, Ivana Malenica

CoRR, 2018

2017

The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification.

Cheng Ju, Aurélien Bibaut, Mark J. van der Laan

CoRR, 2017

2016

The Highly Adaptive Lasso Estimator.

David C. Benkeser, Mark J. van der Laan

Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 2016

Mining with Inference: Data-Adaptive Target Parameters.

Alan E. Hubbard, Mark J. van der Laan

Proceedings of the Handbook of Big Data., 2016

Tutorial for Causal Inference.

Laura Balzer, Maya Petersen, Mark J. van der Laan

Proceedings of the Handbook of Big Data., 2016

2011

Dimension Reduction with Gene Expression Data Using Targeted Variable Importance Measurement.

Hui Wang, Mark J. van der Laan

BMC Bioinform., 2011

2010

A cross-validation deletion-substitution-addition model selection algorithm: Application to marginal structural models.

Thaddeus J. Haight, Yue Wang, Mark J. van der Laan, Ira B. Tager

Comput. Stat. Data Anal., 2010

<i>partDSA</i>: deletion/substitution/addition algorithm for partitioning the covariate space in prediction.

Annette M. Molinaro, Karen Lostritto, Mark J. van der Laan

Bioinform., 2010

2006

Multiple Testing Methods For ChIP - Chip High Density Oligonucleotide Array Data.

Sündüz Keles, Mark J. van der Laan, Sandrine Dudoit, Simon E. Cawley

J. Comput. Biol., 2006

G-computation estimation for causal inference with complex longitudinal data.

Romain Neugebauer, Mark J. van der Laan

Comput. Stat. Data Anal., 2006

Causal effects in longitudinal studies: Definition and maximum likelihood estimation.

Romain Neugebauer, Mark J. van der Laan

Comput. Stat. Data Anal., 2006

A semiparametric model selection criterion with applications to the marginal structural model.

M. Alan Brookhart, Mark J. van der Laan

Comput. Stat. Data Anal., 2006

2005

A Statistical Method for Constructing Transcriptional Regulatory Networks Using Gene Expression and Sequence Data.

Biao Xing, Mark J. van der Laan

J. Comput. Biol., 2005

A causal inference approach for constructing transcriptional regulatory networks.

Biao Xing, Mark J. van der Laan

Bioinform., 2005

Data-adaptive test statistics for microarray data.

Sach Mukherjee, Stephen J. Roberts, Mark J. van der Laan

Proceedings of the ECCB/JBI'05 Proceedings, Fourth European Conference on Computational Biology/Sixth Meeting of the Spanish Bioinformatics Network (Jornadas de BioInformática), Palacio de Congresos, Madrid, Spain, September 28, 2005

2004

Regulatory motif finding by logic regression.

Sündüz Keles, Mark J. van der Laan, Chris Vulpe

Bioinform., 2004

2003

Loss-based estimation with cross-validation: applications to microarray data analysis.

Sandrine Dudoit, Mark J. van der Laan, Sündüz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng

SIGKDD Explor., 2003

Fitting of mixtures with unspecified number of components using cross validation distance estimate.

Maja Miloslavsky, Mark J. van der Laan

Comput. Stat. Data Anal., 2003

Multiple Testing for Gene Expression Data: An Investigation of Null Distributions with Consequences for the Permutation Test.

Katherine S. Pollard, Mark J. van der Laan

Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Scienes, 2003

Estimation of the Bivariate Survival Function with Generalized Bivariate Right Censored Data Structures.

Sündüz Keles, Mark J. van der Laan, James M. Robins

Proceedings of the Advances in Survival Analysis, 2003

Current Status Data: Review, Recent Developments and Open Problems.

Nicholas P. Jewell, Mark J. van der Laan

Proceedings of the Advances in Survival Analysis, 2003

2002

Identification of regulatory elements using a feature selection method.

Sündüz Keles, Mark J. van der Laan, Michael B. Eisen

Bioinform., 2002