Machine Learning

2019
Berk Ustun, Alexander Spanghler, and Yang Liu. 2019. “Actionable Recourse in Linear Classification.” In ACM Conference on Fairness, Accountability, and Transparency (FAT '19). Download
2018
Zhe Feng and Jinglai Li. 5/8/2018. “An adaptive independence sampler MCMC algorithm for infinite dimensional Bayesian inferences.” SIAM Journal on Scientific Computing, 40, 3, Pp. 1301-1321. Download
Shuran Zheng, Bo Waggoner, Yang Liu, and Yiling Chen. 2018. “Active Information Acquisition for Linear Optimization.” In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI-2018). Download
Noah Golowich, Harikrishna Narasimhan, and David C. Parkes. 2018. “Deep Learning for Multi-Facility Location Mechanism Design.” In Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI 2018), Pp. 261-267. Download
Zhe Feng, Harikrishna Narasimhan, and David C. Parkes. 2018. “Deep Learning for Revenue-Optimal Auctions with Budgets.” In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems. (AAMAS 2018), Pp. 354-362. Download
Nir Rosenfeld, Yishai Mansour, and Elad Yom-Tov. 2018. “Discriminative Learning of Prediction Intervals.” The 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018) . Download
Ben Green. 2018. ““Fair” Risk Assessments: A Precarious Approach for Criminal Justice Reform.” In 5th Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2018). Download
Zhe Feng, Chara Podimata, and Vasilis Syrgkanis. 2018. “Learning to Bid Without Knowing your Value.” In Proceedings of the 19th ACM Conference on Economics and Computation (EC'18). Download
Nir Rosenfeld, Eric Balkanski, Amir Globerson, and Yaron Singer. 2018. “Learning to Optimize Combinatorial Functions.” In Proceedings of the 35th International Conference on Machine Learning (ICML 2018). Download
Ben Green and Lily Hu. 2018. “The Myth in the Methodology: Towards a Recontextualization of Fairness in Machine Learning.” In The Debates workshop at the 35th International Conference on Machine Learning (ICML '18). Download
Nir Rosenfeld and Amir Globerson. 2018. “Semi-Supervised Learning with Competitive Infection Models.” The 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018). Download
Yiling Chen, Chara Podimata, Ariel D. Procaccia, and Nisarg Shah. 2018. “Strategyproof Linear Regression in High Dimensions.” In Proceedings of the 19th ACM Conference on Economics and Computation (EC'18). Download
2017
Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal, and David C. Parkes. 2017. “Calibrated fairness in Bandits.” In Proceedings of the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning (Fat/ML 2017). Download
Eric Balkanski, Aviad Rubinstein, and Yaron Singer. 2017. “The Limitations of Optimization from Samples.” In ACM Symposium on the Theory of Computing (STOC 2017). Download
Liu Yang and Yiling Chen. 2017. “Machine Learning aided Peer Prediction.” In Proceedings of the 18th ACM Conference on Economics and Computation (EC-2017). Download
Paul Duetting, Zhe Feng, Harikrishna Narasimhan, and David C. Parkes. 2017. “Optimal Auctions through Deep Learning.” ArXiv e-prints. Download
Hassidim Avinatan and Yaron Singer. 2017. “Robust Guarantees of Stochastic Greedy Algorithms.” In International Conference of Machine Learning (ICML) . Download
Eric Balkanski and Yaron Singer. 2017. “The Sample Complexity of Optimizing a Convex Function.” In Proc. of the Conference on Learning Theory (COLT-17). Download
Avinatan Hassidim and Yaron Singer. 2017. “Submodular Optimization under Noise.” In Proc. of the Conference on Learning Theory (COLT-17). Download
2016
Harikrishna Narasimhan and David C. Parkes. 2016. “A General Statistical Framework for Designing Strategy-proof Assignment Mechanisms.” In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI'16). Download

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