Rui Song Liu, J., Chen, L., Jiang, S., Wang, C., Zhang, S., Liang, J., … Song, R. (2024). A crossword solving system based on Monte Carlo tree search. ARTIFICIAL INTELLIGENCE, 335. https://doi.org/10.1016/j.artint.2024.104192 Shi, C., Wan, R., Song, G., Luo, S., Zhu, H., & Song, R. (2023). A MULTIAGENT REINFORCEMENT LEARNING FRAMEWORK FOR OFF-POLICY EVALUATION IN TWO-SIDED MARKETS. ANNALS OF APPLIED STATISTICS, 17(4), 2701–2722. https://doi.org/10.1214/22-AOAS1700 Gao, Y., Shi, C., & Song, R. (2023). Deep spectral Q-learning with application to mobile health. STAT, 12(1). https://doi.org/10.1002/sta4.564 Shen, Y., Cai, H., & Song, R. (2024, May 29). Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. https://doi.org/10.1080/01621459.2023.2279289 Ghosh, T., Ma, Y., Song, R., & Zhong, P. (2023). Flexible inference of optimal individualized treatment strategy in covariate adjusted randomization with multiple covariates. ELECTRONIC JOURNAL OF STATISTICS, 17(1), 1344–1370. https://doi.org/10.1214/23-EJS2127 Wan, R., Li, Y., Lu, W., & Song, R. (2024). Mining the factor zoo: Estimation of latent factor models with sufficient proxies. JOURNAL OF ECONOMETRICS, 239(2). https://doi.org/10.1016/j.jeconom.2022.08.013 Chen, H., Lu, W., Song, R., & Ghosh, P. (2023, April 12). On Learning and Testing of Counterfactual Fairness through Data Preprocessing. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 4. https://doi.org/10.1080/01621459.2023.2186885 Shi, C., Wang, X., Luo, S., Zhu, H., Ye, J., & Song, R. (2022, March 12). Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 3. https://doi.org/10.1080/01621459.2022.2027776 Pu, W., Hu, J., Wang, X., Li, Y., Hu, S., Zhu, B., … Lyu, S. (2022). Learning a deep dual-level network for robust DeepFake detection. PATTERN RECOGNITION, 130. https://doi.org/10.1016/j.patcog.2022.108832 Shi, C., Zhu, J., Ye, S., Luo, S., Zhu, H., & Song, R. (2022, October 3). Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 10. https://doi.org/10.1080/01621459.2022.2110878 Chen, L., Jiang, S., Liu, J., Wang, C., Zhang, S., Xie, C., … Song, R. (2022). Rule mining over knowledge graphs via reinforcement learning. KNOWLEDGE-BASED SYSTEMS, 242. https://doi.org/10.1016/j.knosys.2022.108371 Ding, Y., Li, Y., & Song, R. (2022, November 18). Statistical Learning for Individualized Asset Allocation. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 11. https://doi.org/10.1080/01621459.2022.2139265 Shi, C., Luo, S., Le, Y., Zhu, H., & Song, R. (2022, September 22). Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 9. https://doi.org/10.1080/01621459.2022.2106868 Zhou, Y., Wang, L., Song, R., & Zhao, T. (2022, June 28). Transformation-Invariant Learning of Optimal Individualized Decision Rules with Time-to-Event Outcomes. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 6. https://doi.org/10.1080/01621459.2022.2068420 Liu, Y., Song, R., Lu, W., & Xiao, Y. (2022, March 10). A Probit Tensor Factorization Model For Relational Learning. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol. 3. https://doi.org/10.1080/10618600.2021.2003204 Shi, C., Song, R., & Lu, W. (2021). Concordance and Value Information Criteria for Optimal Treatment Decision. Annals of Statistics, 49(1), 49–75. https://doi.org/10.1214/19-AOS1908 Cai, H., Song, R., & Lu, W. (2021). GEAR: On optimal decision making with auxiliary data. STAT, 10(1). https://doi.org/10.1002/sta4.399 Wan, R., Zhang, X., & Song, R. (2021). Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control. KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, pp. 1634–1644. https://doi.org/10.1145/3447548.3467303 Chen, X., Song, R., Zhang, J., Adams, S. A., Sun, L., & Lu, W. (2021, August 7). On estimating optimal regime for treatment initiation time based on restricted mean residual lifetime. BIOMETRICS, Vol. 8. https://doi.org/10.1111/biom.13530 Yu, M., Lu, W., & Song, R. (2021). Online Testing of Subgroup Treatment Effects Based on Value Difference. 2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021), pp. 1463–1468. https://doi.org/10.1109/ICDM51629.2021.00189 Shi, C., Zhang, S., Lu, W., & Song, R. (2021, December 22). Statistical inference of the value function for reinforcement learning in infinite-horizon settings. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, Vol. 12. https://doi.org/10.1111/rssb.12465 Yu, M., Lu, W., & Song, R. (2020). A New Framework for Online Testing of Heterogeneous Treatment Effect. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 34(6), 10310–10317. https://doi.org/10.1609/aaai.v34i06.6594 Dong, L., Laber, E., Goldberg, Y., Song, R., & Yang, S. (2020). Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness. STATISTICS IN MEDICINE, 39(25), 3503–3520. https://doi.org/10.1002/sim.8678 Pan, M., Li, Y., Zhou, X., Liu, Z., Song, R., Liu, H., … Tian, Z. (2020). DHPA: Dynamic Human Preference Analytics Framework— A Case Study on Taxi Drivers' Learning Curve Analysis. ACM Transactions on Intelligent Systems and Technology, 11(1). https://doi.org/10.1145/3360312 Yang, S., Kim, J. K., & Song, R. (2020). Doubly robust inference when combining probability and non-probability samples with high dimensional data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 1. https://doi.org/10.1111/rssb.12354 Shi, C., Song, R., Lu, W., & Li, R. (2021). Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 116(535), 1307–1318. https://doi.org/10.1080/01621459.2019.1710154 Chen, H., Lu, W., & Song, R. (2021). Statistical Inference for Online Decision Making: In a Contextual Bandit Setting. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 116(533), 240–255. https://doi.org/10.1080/01621459.2020.1770098 Shi, C., Lu, W., & Song, R. (2020). A Sparse Random Projection-Based Test for Overall Qualitative Treatment Effects. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 115(531), 1201–1213. https://doi.org/10.1080/01621459.2019.1604368 Shi, C., Lu, W., & Song, R. (2019). Determining the Number of Latent Factors in Statistical Multi-Relational Learning. Journal of Machine Learning Research, 20(23), 1–38. Pan, M., Li, Y., Zhou, X., Liu, Z., Song, R., Lu, H., & Luo, J. (2019). Dissecting the Learning Curve of Taxi Drivers: A Data-Driven Approach. In Proceedings of the 2019 SIAM International Conference on Data Mining (pp. 783–791). https://doi.org/10.1137/1.9781611975673.88 Shi, C., Song, R., Chen, Z., & Li, R. (2019). LINEAR HYPOTHESIS TESTING FOR HIGH DIMENSIONAL GENERALIZED LINEAR MODELS. ANNALS OF STATISTICS, 47(5), 2671–2703. https://doi.org/10.1214/18-AOS1761 Su, L., Lu, W., & Song, R. (2019). Modelling and estimation for optimal treatment decision with interference. Stat, 8(1). https://doi.org/10.1002/STA4.219 Shi, C., Song, R., & Lu, W. (2019). ON TESTING CONDITIONAL QUALITATIVE TREATMENT EFFECTS. ANNALS OF STATISTICS, 47(4), 2348–2377. https://doi.org/10.1214/18-AOS1750 Su, L., Lu, W., Song, R., & Huang, D. (2020). Testing and Estimation of Social Network Dependence With Time to Event Data. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 115(530), 570–582. https://doi.org/10.1080/01621459.2019.1617153 Liang, S., Lu, W., & Song, R. (2018). Deep advantage learning for optimal dynamic treatment regime. Statistical Theory and Related Fields, 2(1), 80–88. https://doi.org/10.1080/24754269.2018.1466096 Shi, C., Song, R., & Lu, W. (2018). Discussion of ’Optimal treatment allocations in space and time for on-line control of an emerging infectious disease’ [Review of Optimal treatment allocations in space and time for on-line control of an emerging infectious disease, by E. Laber, N. Meyer, B. Reich, K. Pacifici, J. Collazo, & J. Drake]. Journal of the Royal Statistical Society, Series C, 67(4), 775–776. Jiang, B., Song, R., Li, J., Zeng, D., Lu, W., He, X., … Kallus, N. (2019, October). ENTROPY LEARNING FOR DYNAMIC TREATMENT REGIMES. STATISTICA SINICA, Vol. 29, pp. 1633–1710. https://doi.org/10.5705/ss.202018.0076 Luo, S., Song, R., Styner, M., Gilmore, J. H., & Zhu, H. (2018). FSEM: Functional Structural Equation Models for Twin Functional Data. Journal of the American Statistical Association, 114(525), 344–357. https://doi.org/10.1080/01621459.2017.1407773 Shi, C., Fan, A., Song, R., & Lu, W. (2018). HIGH-DIMENSIONAL A-LEARNING FOR OPTIMAL DYNAMIC TREATMENT REGIMES. ANNALS OF STATISTICS, 46(3), 925–957. https://doi.org/10.1214/17-aos1570 Shi, C., Song, R., Lu, W., & Fu, B. (2018). Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 80(4), 681–702. https://doi.org/10.1111/rssb.12273 Zhu, W., Zeng, D., & Song, R. (2018). Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes. Journal of the American Statistical Association, 114(527), 1404–1417. https://doi.org/10.1080/01621459.2018.1506341 Liang, S. H., Lu, W. B., Song, R., & Wang, L. (2018). Sparse concordance-assisted learning for optimal treatment decision. Journal of Machine Learning Research, 18. Shi, C., Lu, W., & Song, R. (2018). A Massive Data Framework for M-Estimators with Cubic-Rate. Journal of the American Statistical Association, 113(524), 1698–1709. https://doi.org/10.1080/01621459.2017.1360779 Jiang, R., Lu, W., Song, R., Hudgens, M. G., & Naprvavnik, S. (2017). DOUBLY ROBUST ESTIMATION OF OPTIMAL TREATMENT REGIMES FOR SURVIVAL DATA-WITH APPLICATION TO AN HIV/AIDS STUDY. ANNALS OF APPLIED STATISTICS, 11(3), 1763–1786. https://doi.org/10.1214/17-aoas1057 Shi, C., Song, R., & Lu, W. (2017). Discussion of ’Random Projection Ensemble Classification’ [Review of Random Projection Ensemble Classification, by T. Cannings & R. Samworth]. Journal of the Royal Statistical Society, Series B, 79(4), 1021. Liu, Z., Song, R., Zeng, D., & Zhang, J. (2017). Principal components adjusted variable screening. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 110, 134–144. https://doi.org/10.1016/j.csda.2016.12.015 Wang, L., Zhou, Y., Song, R., & Sherwood, B. (2018). Quantile-Optimal Treatment Regimes. Journal of the American Statistical Association, 113(523), 1243–1254. https://doi.org/10.1080/01621459.2017.1330204 Song, R., Luo, S., Zeng, D., Zhang, H. H., Lu, W., & Li, Z. (2017). Semiparametric single-index model for estimating optimal individualized treatment strategy. ELECTRONIC JOURNAL OF STATISTICS, 11(1), 364–384. https://doi.org/10.1214/17-ejs1226 Kang, S., Lu, W., & Song, R. (2017). Subgroup detection and sample size calculation with proportional hazards regression for survival data. Statistics in Medicine, 36(29), 4646–4659. https://doi.org/10.1002/sim.7441 Chen, J. X., Liu, Y. F., Zeng, D. L., Song, R., Zhao, Y. Q., & Kosorok, M. R. (2016). Bayesian nonparametric estimation for dynamic treatment regimes with sequential transition times comment. Journal of the American Statistical Association, 111(515), 942–947. Fan, A., Song, R., & Lu, W. (2017). Change-Plane Analysis for Subgroup Detection and Sample Size Calculation. Journal of the American Statistical Association, 112(518), 769–778. https://doi.org/10.1080/01621459.2016.1166115 Chen, J., Liu, Y., Zeng, D., Song, R., Zhao, Y., & Kosorok, M. R. (2016). Comment. Journal of the American Statistical Association, 111(515), 942–947. https://doi.org/10.1080/01621459.2016.1200914 Fan, C., Lu, W., Song, R., & Zhou, Y. (2016). Concordance-assisted learning for estimating optimal individualized treatment regimes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(5), 1565–1582. https://doi.org/10.1111/rssb.12216 Jiang, R., Lu, W., Song, R., & Davidian, M. (2016). On estimation of optimal treatment regimes for maximizing t -year survival probability. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(4), 1165–1185. https://doi.org/10.1111/rssb.12201 Bai, X., Tsiatis, A., Lu, W., & Song, R. (2017). Optimal treatment regimes for survival endpoints using locally-efficient doubly-robust estimator from a classification perspective. Lifetime Data Analysis, 23(4), 585–604. https://doi.org/10.1007/s10985-016-9376-x Shi, C., Song, R., & Lu, W. (2016). Robust learning for optimal treatment decision with NP-dimensionality. ELECTRONIC JOURNAL OF STATISTICS, 10(2), 2894–2921. https://doi.org/10.1214/16-ejs1178 Fan, A., Lu, W., & Song, R. (2016). SEQUENTIAL ADVANTAGE SELECTION FOR OPTIMAL TREATMENT REGIME. ANNALS OF APPLIED STATISTICS, 10(1), 32–53. https://doi.org/10.1214/15-aoas849 Song, R., Banerjee, M., & Kosorok, M. R. (2016). ASYMPTOTICS FOR CHANGE-POINT MODELS UNDER VARYING DEGREES OF MIS-SPECIFICATION. ANNALS OF STATISTICS, 44(1), 153–182. https://doi.org/10.1214/15-aos1362 Song, R., Kosorok, M., Zeng, D., Zhao, Y., Laber, E., & Yuan, M. (2015). On sparse representation for optimal individualized treatment selection with penalized outcome weighted learning. Stat, 4(1), 59–68. https://doi.org/10.1002/STA4.78 Song, R., Wang, W. W., Zeng, D. L., & Kosorok, M. R. (2015). Penalized q-learning for dynamic treatment regimens. Statistica Sinica, 25(3), 901–920. Bradic, J., & Song, R. (2015). Structured estimation for the nonparametric Cox model. ELECTRONIC JOURNAL OF STATISTICS, 9(1), 492–534. https://doi.org/10.1214/15-ejs1004 Laber, E. B., Zhao, Y.-Q., Regh, T., Davidian, M., Tsiatis, A., Stanford, J. B., … Kosorok, M. R. (2015). Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy. Statistics in Medicine, 35(8), 1245–1256. https://doi.org/10.1002/SIM.6783 Song, R., Lu, W., Ma, S., & Jeng, X. (J. (2014). Censored rank independence screening for high-dimensional survival data. Biometrika, 101(4), 799–814. https://doi.org/10.1093/biomet/asu047 Goldberg, Y., Song, R., Zeng, D. L., & Kosorok, M. R. (2014). Comment on "Dynamic treatment regimes: Technical challenges and applications". Electronic Journal of Statistics, 8, 1290–1300. Song, R., Kosorok, M. R., & Fine, J. P. (2014). Comment on ”Multiscale change point inference” by Frick, Munk and Sieling [Review of Multiscale change point inference, by K. Frick, A. Munk, & H. Sieling]. Journal of the Royal Statistical Society, Series B, 76(3), 564. Zhao, Y. Q., Zeng, D., Laber, E. B., Song, R., Yuan, M., & Kosorok, M. R. (2014). Doubly robust learning for estimating individualized treatment with censored data. Biometrika, 102(1), 151–168. https://doi.org/10.1093/biomet/asu050 Song, R., Yi, F., & Zou, H. (2014). On varying-coefficient independence screening for high-dimensional varying-coefficient models. Statistica Sinica, 24(4), 1735–1752. Goldberg, Y., Song, R., & Kosorok, M. R. (2013). Adaptive Q-learning. In M. Bannerjee, F. Bunea, J. Huang, V. Koltchinskii, & M. H. Maathuis (Eds.), From Probability to Statistics and Back: High-Dimensional Models and Processes (pp. 150–162). Beachwood, Ohio: Institute of Mathematical Statistics. Song, R., Huang, J., & Ma, S. (2012). Integrative Prescreening in Analysis of Multiple Cancer Genomic Studies. BMC Bioinformatics, 13(168). https://doi.org/10.1186/1471-2105-13-168 Fan, J., Feng, Y., & Song, R. (2011). Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models. Journal of the American Statistical Association, 106(494), 544–557. https://doi.org/10.1198/jasa.2011.tm09779 Zhou, H., Song, R., Wu, Y., & Qin, J. (2010). Statistical Inference for a Two-Stage Outcome-Dependent Sampling Design with a Continuous Outcome. Biometrics, 67(1), 194–202. https://doi.org/10.1111/j.1541-0420.2010.01446.x Fan, J., & Song, R. (2010). Sure independence screening in generalized linear models with NP-dimensionality. The Annals of Statistics, 38(6), 3567–3604. https://doi.org/10.1214/10-aos798 Song, R., Zhou, H., & Kosorok, M. R. (2009). A note on semiparametric efficient inference for two-stage outcome-dependent sampling with a continuous outcome. Biometrika, 96(1), 221–228. https://doi.org/10.1093/biomet/asn073 Anand, I. S., Carson, P., Galle, E., Song, R., Boehmer, J., Ghali, J. K., … Bristow, M. R. (2009). Cardiac Resynchronization Therapy Reduces the Risk of Hospitalizations in Patients With Advanced Heart Failure. Circulation, 119(7), 969–977. https://doi.org/10.1161/circulationaha.108.793273 Song, R., & Cai, J. (2009). Joint covariate-adjusted score test statistics for recurrent events and a terminal event. Lifetime Data Analysis, 16(4), 491–508. https://doi.org/10.1007/s10985-009-9140-6 Song, R., Kosorok, M. R., & Fine, J. P. (2009). On asymptotically optimal tests under loss of identifiability in semiparametric models. The Annals of Statistics, 37(5A), 2409–2444. https://doi.org/10.1214/08-aos643 Meunier, J., Song, R., Lutz, R. S., Andersen, D. E., Doherty, K. E., Bruggink, J. G., & Oppelt, E. (2008). Proximate Cues for a Short-Distance Migratory Species: an Application of Survival Analysis. Journal of Wildlife Management, 72(2), 440–448. https://doi.org/10.2193/2006-521 Song, R., Cook, T. D., & Kosorok, M. R. (2008). What We Want versus What We Can Get: A Closer Look at Failure Time Endpoints for Cardiovascular Studies. Journal of Biopharmaceutical Statistics, 18(2), 370–381. https://doi.org/10.1080/10543400701697224 Kosorok, M. R., & Song, R. (2007). Inference under right censoring for transformation models with a change-point based on a covariate threshold. The Annals of Statistics, 35(3), 957–989. https://doi.org/10.1214/009053606000001244 Song, R., Kosorok, M. R., & Cai, J. (2007). Robust Covariate-Adjusted Log-Rank Statistics and Corresponding Sample Size Formula for Recurrent Events Data. Biometrics, 64(3), 741–750. https://doi.org/10.1111/j.1541-0420.2007.00948.x