Wang, Kang, Xu, Renzhe, Li, Bo. (2026). Lower Bias, Higher Welfare: How Creator Competition Reshapes Bias-Variance Tradeoff in Recommendation Platforms? KDD 2026.
Shi, Bowen, Mao, Xiaojie, Yang, Mochen, Li, Bo. (2025). What, Why, and How: An Empiricist’s Guide to Double/Debiased Machine Learning. Information Systems Research, forthcoming.
Xu, Renzhe, Wang, Haotian, Zhang, Xingxuan, Li, Bo, Cui, Peng. (2025). Ppa-game: Characterizing and learning competitive dynamics among online content creators. KDD 2025.
Xu, Renzhe, Wang, Kang, Li, Bo. (2025). Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sources. ICML 2025.
Chhabra, Anshuman, Li, Bo, Chen, Jian, Mohapatra, Prasant, Liu, Hongfu. (2024). Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models. ICML 2025 (Oral).
Zhu, Minqin, Wu, Anpeng, Li, Haoxuan, Xiong, Ruoxuan, Li, Bo, Wu, Fei, Kuang, Kun. (2025). Learning double balancing representation for heterogeneous dose-response curve estimation. Neural Networks, 107600.
Zhao, Ziyu, Wu, Anpeng, Kuang, Kun, Xiong, Ruoxuan, Li, Bo, Wang, Zhihua, Wu, Fei. (2025). Networked Instrumental Variable for Treatment Effect Estimation With Unobserved Confounders. IEEE Transactions on Knowledge and Data Engineering (TDKE), 32(2), 823-836.
Liu, Jiashuo, Wu, Jiayun, Wang, Tianyu, Zou, Hao, Li, Bo, Cui, Peng. (2023). Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications. ICML 2024.
Zhu, Minqin, Wu, Anpeng, Li, Haoxuan, Xiong, Ruoxuan, Li, Bo, Yang, Xiaoqing, Qin, Xuan, Zhen, Peng, Guo, Jiecheng, Wu, Fei. (2024). Contrastive balancing representation learning for heterogeneous dose-response curves estimation. AAAI 2024.
Liu, Jiashuo, Wu, Jiayun, Peng, Jie, Wu, Xiaoyu, Zheng, Yang, Li, Bo, Cui, Peng. (2024). Enhancing Distributional Stability among Sub-populations. Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTAT) 2024.
Zhao, Ziyu, Kuang, Kun, Li, Bo, Cui, Peng, Wu, Runze, Xiao, Jun, Wu, Fei. (2023). Differentiated matching for individual and average treatment effect estimation. Data Mining and Knowledge Discovery, 37(1), 205-227.
Kuang, Kun, Wang, Haotian, Liu, Yue, Xiong, Ruoxuan, Wu, Runze, Lu, Weiming, Zhuang, Yueting, Wu, Fei, Cui, Peng, Li, Bo. (2023). Stable prediction with leveraging seed variable. IEEE Transactions on Knowledge and Data Engineering (TDKE), 35(6), 6392-6404.
Wu, Anpeng, Yuan, Junkun, Kuang, Kun, Li, Bo, Wu, Runze, Zhu, Qiang, Zhuang, Yueting, Wu, Fei. (2023). Learning decomposed representations for treatment effect estimation. IEEE Transactions on Knowledge and Data Engineering (TDKE), 35(5), 4989-5001.
Liu, Jiashuo, Shen, Zheyan, Cui, Peng, Zhou, Linjun, Kuang, Kun, Li, Bo. (2023). Distributionally robust learning with stable adversarial training. IEEE Transactions on Knowledge and Data Engineering (TDKE), 35(11), 11288-11300.
Chen, Qianyi, Li, Bo, Deng, Lu, Wang, Yong. (2023). Optimized covariance design for ab test on social network under interference. NeurIPS 2023.
Wu, Anpeng, Kuang, Kun, Xiong, Ruoxuan, Li, Bo, Wu, Fei. (2023). Stable estimation of heterogeneous treatment effects. ICML 2023.
Xu, Renzhe, Wang, Haotian, Zhang, Xingxuan, Li, Bo, Cui, Peng. (2023). Competing for Shareable Arms in Multi-Player Multi-Armed Bandits. ICML 2023.
Wu, Anpeng, Kuang, Kun, Xiong, Ruoxuan, Zhu, Minqin, Liu, Yuxuan, Li, Bo, Liu, Furui, Wang, Zhihua, Wu, Fei. (2023). Learning instrumental variable from data fusion for treatment effect estimation. AAAI 2023.
Liu, Jiashuo, Wu, Jiayun, Pi, Renjie, Xu, Renzhe, Zhang, Xingxuan, Li, Bo, Cui, Peng. (2023). Measure the predictive heterogeneity. ICLR 2023.
Zou, Hao, Wang, Haotian, Xu, Renzhe, Li, Bo, Pei, Jian, Jian, Ye Jun, Cui, Peng. (2023). Factual observation based heterogeneity learning for counterfactual prediction. Conference on Causal Learning and Reasoning 2023.
Kuang, Kun, Cui, Peng, Zou, Hao, Li, Bo, Tao, Jianrong, Wu, Fei, Yang, Shiqiang. (2022). Data-driven Variable Decomposition for Treatment Effect Estimation. IEEE Transactions on Knowledge and Data Engineering (TKDE), 34(5), 2120–2134.
Yuan, Junkun, Wu, Anpeng, Kuang, Kun, Li, Bo, Wu, Runze, Wu, Fei, Lin, Lanfen. (2022). Auto IV: Counterfactual prediction via automatic instrumental variable decomposition. ACM Transactions on Knowledge Discovery from Data (TKDD), 16(4), 1-20.
Xu, Renzhe, Zhang, Xingxuan, Li, Bo, Zhang, Yafeng, Chen, Xiaolong, Cui, Peng. (2022). Product Ranking for Revenue Maximization with Multiple Purchases. NeurIPS 2022.
Liu, Jiashuo, Wu, Jiayun, Li, Bo, Cui, Peng. (2022). Distributionally Robust Optimization with Data Geometry. NeurIPS 2022.
Zou, Hao, Li, Bo, Han, Jiangang, Chen, Shuiping, Ding, Xuetao, Cui, Peng. (2022). Counterfactual prediction for outcome-oriented treatments. ICML 2022.
Wu, Anpeng, Kuang, Kun, Li, Bo, Wu, Fei. (2022). Instrumental variable regression with confounder balancing. ICML 2022.
Xu, Renzhe, Zhang, Xingxuan, Li, Bo, Zhang, Yafeng, Chen, Xiaolong, Cui, Peng. (2022). Regulatory instruments for fair personalized pricing. WWW 2022.
Kuang, Kun, Li, Yunzhe, Li, Bo, Cui, Peng, Yang, Hongxia, Tao, Jianrong, Wu, Fei. (2021). Continuous treatment effect estimation via generative adversarial de-confounding. Data Mining and Knowledge Discovery, 35(6), 2467-2497.
Liu, Jiashuo, Hu, Zheyuan, Cui, Peng, Li, Bo, Shen, Zheyan. (2021). Kernelized Heterogeneous Risk Minimization. NeurIPS 2021.
Liu, Jiashuo, Hu, Zheyuan, Cui, Peng, Li, Bo, Shen, Zheyan. (2021). Heterogeneous risk minimization. ICML 2021 (Spotlight).
Liu, Jiashuo, Shen, Zheyan, Cui, Peng, Zhou, Linjun, Kuang, Kun, Li, Bo, Lin, Yishi. (2021). Stable adversarial learning under distributional shifts. AAAI 2021.
Gu, Xinyue, Li, Bo. (2020). Cross-estimation for decision selection. Applied Stochastic Models in Business and Industry, 36(5), 932-958.
Zou, Hao, Cui, Peng, Li, Bo, Shen, Zheyan, Ma, Jianxin, Yang, Hongxia, He, Yue. (2020). Counterfactual Prediction for Bundle Treatment. NeurIPS 2020.
Xu, Renzhe, Cui, Peng, Kuang, Kun, Li, Bo, Zhou, Linjun, Shen, Zheyan, Cui, Wei. (2020). Algorithmic decision making with conditional fairness. KDD 2020.
Shen, Zheyan, Cui, Peng, Liu, Jiashuo, Zhang, Tong, Li, Bo, Chen, Zhitang. (2020). Stable learning via differentiated variable decorrelation. KDD 2020.
Kuang, Kun, Xiong, Ruoxuan, Cui, Peng, Athey, Susan, Li, Bo. (2020). Stable prediction with model misspecification and agnostic distribution shift. AAAI 2020.
Kuang, Kun, Cui, Peng, Li, Bo, Jiang, Meng, Wang, Yashen, Wu, Fei, Yang, Shiqiang. (2019). Treatment effect estimation via differentiated confounder balancing and regression. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(1), 1-25.
Li, Bo, Liang, Baosheng, Tong, Xingwei, Sun, Jianguo. (2019). On estimation of partially linear varying-coefficient transformation models with censored data. Statistica Sinica, 29(4), 1963-1975.
Wang, Hao, Li, Bo, Tong, Seung Hoon, Chang, In-Kap, Wang, Kaibo. (2018). A discrete spatial model for wafer yield prediction. Quality Engineering, 30(2), 169-182.
Kuang, Kun, Cui, Peng, Athey, Susan, Xiong, Ruoxuan, Li, Bo. (2018). Stable prediction across unknown environments. KDD 2018.
Shen, Zheyan, Cui, Peng, Kuang, Kun, Li, Bo, Chen, Peixuan. (2018). Causally regularized learning with agnostic data selection bias. ACM MM 2018.
Kuang, Kun, Cui, Peng, Li, Bo, Jiang, Meng, Yang, Shiqiang. (2017). Estimating treatment effect in the wild via differentiated confounder balancing. KDD 2017.
Kuang, Kun, Cui, Peng, Li, Bo, Jiang, Meng, Yang, Shiqiang, Wang, Fei. (2017). Treatment effect estimation with data-driven variable decomposition. AAAI 2017.
He, Luo, Xie, Xiaolei, Liu, Hongyan, Li, Bo. (2017). How out-of-pocket ratio influences readmission: An analysis based on front sheet of inpatient medical record. International Conference on Smart Health, 67-78.
Wang, Kaibo, Jiang, Wei, Li, Bo. (2016). A spatial variable selection method for monitoring product surface. International Journal of Production Research, 54(14), 4161-4181.
Tao, Jialing, Wang, Kaibo, Li, Bo, Liu, Liang, Cai, Qi. (2016). Hierarchical models for the spatial-temporal carbon nanotube height variations. International Journal of Production Research, 54(21), 6613-6632.
Liu, Angela, Mazumdar, Tridib, Li, Bo. (2015). Counterfactual decomposition of movie star effects with star selection. Management Science, 61(7), 1704-1721.
Li, Bo, Wang, Chi, Wei, Li, Bo. (2014). Trends in China’s gender employment and pay gap: Estimating gender pay gaps with employment selection. Journal of Comparative Economics, 42(3), 708-725.
Wang, Kaibo, Yeh, Arthur B, Li, Bo. (2014). Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation. Computational Statistics & Data Analysis, 78, 206-217.
Li, Bo, Yeh, Arthur B. (2013). Monitoring the covariance matrix via penalized likelihood estimation. IIE Transactions, 45(2), 132-146.
Yeh, Arthur B, Li, Bo, Wang, Kaibo. (2012). Monitoring multivariate process variability with individual observations via penalised likelihood estimation. International Journal of Production Research, 50(22), 6624-6638.
Chi, Wei, Li, Bo, Yu, Qiumei. (2011). Decomposition of the increase in earnings inequality in urban China: A distributional approach. China Economic Review, 22(3), 299-312.
Leng, Chenlei, Li, Bo. (2011). Forward adaptive banding for estimating large covariance matrices. Biometrika, 98(4), 821-830.
Leng, Chenlei, Li, Bo. (2010). Least squares approximation with a diverging number of parameters. Statistics & Probability Letters, 80(3-4), 254-261.
Li, Bo. (2009). Asymptotically distribution-free goodness-of-fit testing: A unifying view. Econometric Reviews, 28(6), 632-657.
Wang, Hansheng, Li, Bo, Leng, Chenlei. (2009). Shrinkage tuning parameter selection with a diverging number of parameters. Journal of the Royal Statistical Society Series B: Statistical Methodology, 71(3), 671-683.
Chi, Wei, Li, Bo. (2008). Glass ceiling or sticky floor? Examining the gender earnings differential across the earnings distribution in urban China, 1987-2004. Journal of Comparative Economics, 36(2), 243-263.
Li, Bo. (2008). Nonparametric testing of an exclusion restriction in quantile regression. Communications in Statistics—Theory and Methods, 37(18), 2877-2889.
Bickel, Peter, Li, Bo, Bengtsson, Thomas. (2008). Sharp failure rates for the bootstrap particle filter in high dimensions. In Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh (Vol. 3, pp. 318-330). Institute of Mathematical Statistics Collections.
Bengtsson, Thomas, Bickel, Peter, Li, Bo. (2008). Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems. In Probability and Statistics: Essays in Honor of David A. Freedman (Vol. 2, pp. 316-335). Institute of Mathematical Statistics Collections.
Bickel, Peter J, Li, Bo. (2007). Local polynomial regression on unknown manifolds. Institute of Mathematical Statistics Lecture Notes-Monograph Series, 177-186.
Bickel, Peter J, Li, Bo. (2006). Regularization in statistics (with discussion and rejoinder). Test, 15(2), 303-344.
