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Transfer Learning Under High-Dimensional Network Convolutional Regression Model (Submitted).
Liyuan Wang, Jiachen Chen, Kathryn L Lunetta, Danyang Huang, Huimin Cheng, and Debarghya Mukherjee.
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A Debiased Estimator for the Mediation Functional in Ultra-High-Dimensional Setting in the Presence of Interaction Effects (Submitted).
Shi Bo, AmirEmad Ghassami, and Debarghya Mukherjee.
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On the estimation rate of Bayesian PINN for inverse problems (Under Major revision at Bernoulli).
Yi Sun, Debarghya Mukherjee, and Yves Atchade.
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Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification (Submitted).
Jiachen Chen, Danyang Huang, Liyuan Wang, Kathryn L. Lunetta, Debarghya Mukherjee, and Huimin Cheng.
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Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift ("Accepted" at NeurRIPS, 2024).
Jiawei Ge, Debarghya Mukherjee, and Jianqing Fan.
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Minimax Optimal rates of convergence in the shuffled regression, unlinked regression, and deconvolution under vanishing noise ("Minor Revision" at Bernoulli).
Cecile Durot, Debarghya Mukherjee.
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UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation (Submitted).
Nabarun Deb, Debarghya Mukherjee
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Trade-off Between Dependence and Complexity for Nonparametric Learning -- an Empirical Process Approach (Submitted).
Jiawei Ge, Debarghya Mukherjee, and Jianqing Fan.
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Deep Neural Networks for Nonparametric Interaction Models with Diverging Dimension ("Accepted" at Annals of Statistics).
Sohom Bhattacharya, Jianqing Fan, and Debarghya Mukherjee.
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Predictor-corrector algorithms for stochastic optimization under gradual distribution shift ("Accepted" at ICLR, 2023).
Subha Maity*, Debarghya Mukherjee*, Moulinath Banerjee, and Yuekai Sun. (* = equal contribution)
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Domain adaptation meets Individual Fairness. And they get along ("Accepted" at NeurIPS, 2022).
Debarghya Mukherjee*, Felix Petersen*, Yuekai Sun, and Mikhail Yurochkin. (* = equal contribution)
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On robust learning in the canonical change point problem under heavy-tailed errors in finite and growing dimensions ("Accepted" at Electronic Journal of Statistics).
Debarghya Mukherjee, Moulinath Banerjee, and Ya'acov Ritov.
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Post-processing for Individual Fairness ("Accepted" at NeurIPS, 2021).
Felix Petersen*, Debarghya Mukherjee*, Yuekai Sun, and Mikhail Yurochkin. (* = equal contribution)
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Estimation of a score-explained non-randomized treatment effect in fixed and high dimensions ("Accepted" at Bernoulli).
Debarghya Mukherjee, Moulinath Banerjee, and Ya'acov Ritov.
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Outlier robust optimal transport ("Accepted" at ICML, 2021).
Debarghya Mukherjee, Aritra Guha, Justin Solomon, Yuekai Sun, and Mikhail Yurochkin.
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There is no trade-off: enforcing fairness can improve accuracy ("Accepted" at NeurIPS, 2021).
Subha Maity*, Debarghya Mukherjee*, Yuekai Sun, and Mikhail Yurochkin. (* = equal contribution)
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Markovian And Non-Markovian Processes with Active Decision Making Strategies For Addressing The COVID-19 Pandemic (Submitted).
Hamid Eftekhari*, Debarghya Mukherjee*, Moulinath Banerjee, and Ya'acov Ritov. (* = equal contribution)
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Two Simple Ways to Learn Individual Fairness Metrics from Data ("Accepted" at ICML, 2020).
Debarghya Mukherjee*, Mikhail Yurochkin*, Moulinath Banerjee, and Yuekai Sun. (* = equal contribution)
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Asymptotic normality of a linear threshold estimator in fixed dimension with near-optimal rate ("Accepted" at Electronic Journal of Statistics).
Debarghya Mukherjee, Moulinath Banerjee, and Ya'acov Ritov.
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Optimal linear discriminators for the discrete choice model in growing dimensions ("Accepted" at Annals of Statistics).
Debarghya Mukherjee, Moulinath Banerjee, and Ya'acov Ritov.