Xiaojie Mao

According to our database, Xiaojie Mao authored at least 20 papers between 2018 and 2023.

Collaborative distances:

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

2023

Stochastic Optimization Forests.

Nathan Kallus, Xiaojie Mao

Manag. Sci., April, 2023

Source Condition Double Robust Inference on Functionals of Inverse Problems.

Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara

CoRR, 2023

Learning under Selective Labels with Data from Heterogeneous Decision-makers: An Instrumental Variable Approach.

Jian Chen, Zhehao Li, Xiaojie Mao

CoRR, 2023

Online Joint Assortment-Inventory Optimization under MNL Choices.

Yong Liang, Xiaojie Mao, Shiyuan Wang

CoRR, 2023

Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness.

Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara

CoRR, 2023

Minimax Instrumental Variable Regression and L₂ Convergence Guarantees without Identification or Closedness.

Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Inference on Strongly Identified Functionals of Weakly Identified Functions.

Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regret Regimes.

Yichun Hu, Nathan Kallus, Xiaojie Mao

Oper. Res., November, 2022

Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination.

Nathan Kallus, Xiaojie Mao, Angela Zhou

Manag. Sci., 2022

Fast Rates for Contextual Linear Optimization.

Yichun Hu, Nathan Kallus, Xiaojie Mao

Manag. Sci., 2022

Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning.

Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou

Proceedings of the International Conference on Machine Learning, 2022

2021

Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach.

Nathan Kallus, Xiaojie Mao, Masatoshi Uehara

CoRR, 2021

2020

On the role of surrogates in the efficient estimation of treatment effects with limited outcome data.

Nathan Kallus, Xiaojie Mao

CoRR, 2020

Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes.

Yichun Hu, Nathan Kallus, Xiaojie Mao

Proceedings of the Conference on Learning Theory, 2020

2019

Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects, Conditional Value at Risk, and Beyond.

Nathan Kallus, Xiaojie Mao, Masatoshi Uehara

CoRR, 2019

Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved.

Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, Madeleine Udell

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding.

Nathan Kallus, Xiaojie Mao, Angela Zhou

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network.

Zhiyu Qu, Xiaojie Mao, Zhian Deng

IEEE Access, 2018

Causal Inference with Noisy and Missing Covariates via Matrix Factorization.

Nathan Kallus, Xiaojie Mao, Madeleine Udell

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Radar Signal Waveform Recognition Based on Convolutional Denoising Autoencoder.

Zhaolei Liu, Xiaojie Mao, Zhian Deng

Proceedings of the Communications, Signal Processing, and Systems, 2018