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Mark J. van der Laan
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- affiliation: University of California, Berkeley, USA
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2020 – today
- 2024
- [c9]Toru Shirakawa, Yi Li, Yulun Wu, Sky Qiu, Yuxuan Li, Mingduo Zhao, Hiroyasu Iso, Mark J. van der Laan:
Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer. ICML 2024 - [i19]Toru Shirakawa, Yi Li, Yulun Wu, Sky Qiu, Yuxuan Li, Mingduo Zhao, Hiroyasu Iso, Mark J. van der Laan:
Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer. CoRR abs/2404.04399 (2024) - [i18]Razieh Nabi, Nima S. Hejazi, Mark J. van der Laan, David C. Benkeser:
Statistical learning for constrained functional parameters in infinite-dimensional models with applications in fair machine learning. CoRR abs/2404.09847 (2024) - [i17]Ahmed Alaa, Rachael V. Phillips, Emre Kiciman, Laura Balzer, Mark J. van der Laan, Maya Petersen:
Large Language Models as Co-Pilots for Causal Inference in Medical Studies. CoRR abs/2407.19118 (2024) - 2023
- [j20]Philippe Boileau
, Nima S. Hejazi
Cross-Validated Loss-based Covariance Matrix Estimator Selection in High Dimensions. J. Comput. Graph. Stat. 32(2): 601-612 (2023) - [j19]David McCoy
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
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) - [c8]Ahmed M. Alaa, Zaid Ahmad, Mark J. van der Laan:
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects. NeurIPS 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) - [i15]Ahmed Alaa, Zaid Ahmad, Mark J. van der Laan:
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects. CoRR abs/2308.14895 (2023) - 2022
- [j17]Nima S. Hejazi
'haldensify': Highly adaptive lasso conditional density estimation in 'R'. J. Open Source Softw. 7(78): 4522 (2022) - [j16]Gilmer Valdes
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
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
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) - [i9]Mark J. van der Laan, Sherri Rose:
Why Machine Learning Cannot Ignore Maximum Likelihood Estimation. CoRR abs/2110.12112 (2021) - 2020
- [j14]Nima S. Hejazi
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
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)
2010 – 2019
- 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
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
The Highly Adaptive Lasso Estimator. DSAA 2016: 689-696 - [p4]Laura Balzer, Maya Petersen, Mark J. van der Laan:
Tutorial for Causal Inference. Handbook of Big Data 2016: 361-386 - [p3]Alan E. Hubbard, Mark J. van der Laan:
Mining with Inference: Data-Adaptive Target Parameters. Handbook of Big Data 2016: 439-452 - [e1]Peter Bühlmann, Petros Drineas, Michael J. Kane, Mark J. van der Laan:
Handbook of Big Data. Chapman and Hall/CRC 2016, ISBN 978-1-4822-4907-1 [contents] - 2011
- [j13]Hui Wang, Mark J. van der Laan:
Dimension Reduction with Gene Expression Data Using Targeted Variable Importance Measurement. BMC Bioinform. 12: 312 (2011) - 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) - [j11]Thaddeus J. Haight, Yue Wang, Mark J. van der Laan, Ira B. Tager:
A cross-validation deletion-substitution-addition model selection algorithm: Application to marginal structural models. Comput. Stat. Data Anal. 54(12): 3080-3094 (2010)
2000 – 2009
- 2006
- [j10]M. Alan Brookhart, Mark J. van der Laan:
A semiparametric model selection criterion with applications to the marginal structural model. Comput. Stat. Data Anal. 50(2): 475-498 (2006) - [j9]Romain Neugebauer, Mark J. van der Laan:
Causal effects in longitudinal studies: Definition and maximum likelihood estimation. Comput. Stat. Data Anal. 51(3): 1664-1675 (2006) - [j8]Romain Neugebauer, Mark J. van der Laan:
G-computation estimation for causal inference with complex longitudinal data. Comput. Stat. Data Anal. 51(3): 1676-1697 (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) - 2005
- [j6]Biao Xing, Mark J. van der Laan:
A causal inference approach for constructing transcriptional regulatory networks. Bioinform. 21(21): 4007-4013 (2005) - [j5]Biao Xing, Mark J. van der Laan:
A Statistical Method for Constructing Transcriptional Regulatory Networks Using Gene Expression and Sequence Data. J. Comput. Biol. 12(2): 229-246 (2005) - [c2]Sach Mukherjee, Stephen J. Roberts, Mark J. van der Laan:
Data-adaptive test statistics for microarray data. ECCB/JBI 2005: 114 - 2004
- [j4]Sündüz Keles, Mark J. van der Laan, Chris Vulpe
Regulatory motif finding by logic regression. Bioinform. 20(16): 2799-2811 (2004) - 2003
- [j3]Maja Miloslavsky, Mark J. van der Laan:
Fitting of mixtures with unspecified number of components using cross validation distance estimate. Comput. Stat. Data Anal. 41(3-4): 413-428 (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) - [c1]Katherine S. Pollard, Mark J. van der Laan:
Multiple Testing for Gene Expression Data: An Investigation of Null Distributions with Consequences for the Permutation Test. METMBS 2003: 3-9 - [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 - [p1]Nicholas P. Jewell, Mark J. van der Laan:
Current Status Data: Review, Recent Developments and Open Problems. Advances in Survival Analysis 2003: 625-642 - 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)
Coauthor Index
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last updated on 2024-09-04 00:32 CEST by the dblp team
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