1. Jacob D Abernethy , Pranjal Awasthi , Matthäus Kleindessner , Jamie Morgenstern , Chris Russell , and Jie Zhang . 2022 . Active Sampling for Min-Max Fairness . In Proceedings of the 39th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 162) , Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (Eds.). PMLR, 53–65. https://proceedings.mlr.press/v162/abernethy22a.html Jacob D Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, and Jie Zhang. 2022. Active Sampling for Min-Max Fairness. In Proceedings of the 39th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (Eds.). PMLR, 53–65. https://proceedings.mlr.press/v162/abernethy22a.html
2. Fair active learning
3. On the measurement of inequality
4. Beyond Reasonable Doubt
5. Matthew Blackwell , Nicole E. Pashley , and Dominic Valentino . 2022. Batch Adaptive Designs to Improve Efficiency in Social Science Experiments. https://www.mattblackwell.org/files/papers/batch_adaptive.pdf. Accessed 8 November 2022 . Matthew Blackwell, Nicole E. Pashley, and Dominic Valentino. 2022. Batch Adaptive Designs to Improve Efficiency in Social Science Experiments. https://www.mattblackwell.org/files/papers/batch_adaptive.pdf. Accessed 8 November 2022.