Radar Signal Waveform Recognition Based on Convolutional Denoising Autoencoder. - Zhaolei Liu; Xiaojie Mao; Zhian Deng (CSPS (2), 2018)

Causal Inference with Noisy and Missing Covariates via Matrix Factorization. - Nathan Kallus; Xiaojie Mao; Madeleine Udell (NeurIPS, 2018)

Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network. - Zhiyu Qu; Xiaojie Mao; Zhian Deng (IEEE Access, 2018)

Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding. - Nathan Kallus; Xiaojie Mao; Angela Zhou (AISTATS, 2019)

Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved. - Jiahao Chen 001; Nathan Kallus; Xiaojie Mao; Geoffry Svacha; Madeleine Udell (FAT, 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)

Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes. - Yichun Hu; Nathan Kallus; Xiaojie Mao (COLT, 2020)

On the role of surrogates in the efficient estimation of treatment effects with limited outcome data. - Nathan Kallus; Xiaojie Mao (CoRR, 2020)

Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach. - Nathan Kallus; Xiaojie Mao; Masatoshi Uehara (CoRR, 2021)

Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning. - Nathan Kallus; Xiaojie Mao; Kaiwen Wang; Zhengyuan Zhou (ICML, 2022)

Fast Rates for Contextual Linear Optimization. - Yichun Hu; Nathan Kallus; Xiaojie Mao (Manag. Sci., 2022)

Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination. - Nathan Kallus; Xiaojie Mao; Angela Zhou (Manag. Sci., 2022)

Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regret Regimes. - Yichun Hu; Nathan Kallus; Xiaojie Mao (Oper. Res., 2022)

Inference on Strongly Identified Functionals of Weakly Identified Functions. - Andrew Bennett; Nathan Kallus; Xiaojie Mao; Whitney Newey; Vasilis Syrgkanis; Masatoshi Uehara (COLT, 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 (COLT, 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)

Online Joint Assortment-Inventory Optimization under MNL Choices. - Yong Liang; Xiaojie Mao; Shiyuan Wang (CoRR, 2023)

Learning under Selective Labels with Data from Heterogeneous Decision-makers: An Instrumental Variable Approach. - Jian Chen; Zhehao Li; Xiaojie Mao (CoRR, 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)

Stochastic Optimization Forests. - Nathan Kallus; Xiaojie Mao (Manag. Sci., 2023)

Contextual Linear Optimization with Bandit Feedback. - Yichun Hu; Nathan Kallus; Xiaojie Mao; Yanchen Wu (NeurIPS, 2024)

Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond. - Nathan Kallus; Xiaojie Mao; Masatoshi Uehara (J. Mach. Learn. Res., 2024)