Conference papers

Optimal nuisance function tuning for estimating a doubly robust functional under proportional asymptotics. McGrath, S., Mukherjee, D., Mukherjee, R., and Wang, Z. J. arXiv preprint arXiv:2509.25536, 2025.

Transfer learning on edge connecting probability estimation under graphon model. Wang, Y., Cheng, Y.-H., Mukherjee, D., and Cheng, H. arXiv preprint arXiv:2510.05527, 2025.

Domain adaptation meets individual fairness. And they get along. Mukherjee, D., Petersen, F., Yurochkin, M., and Sun, Y. Advances in Neural Information Processing Systems, 2022, 28902–28913.

Predictor-corrector algorithms for stochastic optimization under gradual distribution shift. Maity, S., Mukherjee, D., Banerjee, M., and Sun, Y. arXiv preprint arXiv:2205.13575, 2022.

Two simple ways to learn individual fairness metrics from data. Mukherjee, D., Yurochkin, M., Banerjee, M., and Sun, Y. International conference on machine learning, PMLR, 2020, 7097–7107.

There is no trade-off: Enforcing fairness can improve accuracy. Maity, S., Mukherjee, D., Yurochkin, M., and Sun, Y. stat, 2020, 6.

Outlier-robust optimal transport. Mukherjee, D., Guha, A., Solomon, J. M., Sun, Y., and Yurochkin, M. International conference on machine learning, PMLR, 2021, 7850–7860.

Post-processing for individual fairness. Petersen, F., Mukherjee, D., Sun, Y., and Yurochkin, M. Advances in Neural Information Processing Systems, 2021, 25944–25955.

Does enforcing fairness mitigate biases caused by subpopulation shift? Maity, S., Mukherjee, D., Yurochkin, M., and Sun, Y. Advances in Neural Information Processing Systems, 2021, 25773–25784.