Machine Learning

2020
Nir Rosenfeld, Aron Szanto, and David C. Parkes. 2020. “From How to What: Inferring Rumor Content from Patterns of Information Propagation.” In Proceedings of the 29th World Wide Web Conference (WWW 2020).
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
Christos Dimitrakakis, Yang Liu, David C. Parkes, and Goran Radanovic. 2019. “Bayesian Fairness.” In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI). Download
Berk Ustun, Yang Liu, and David C. Parkes. 2019. “Fairness without Harm: Decoupled Classifiers with Preference Guarantees.” In Proceedings of the 36th International Conference on Machine Learning (ICML'19), Pp. 6373-6382. Download
Jack Serrino, Max Kleiman-Weiner, David C. Parkes, and Joshua B. Tenenbaum. 2019. “Finding Friend and Foe in Multi-Agent Games.” In Proceedings of the 32nd Annual Conf. on Neural Information Processing Systems 2019, (NeurIPS 2019), Pp. 1249-1259. Download
Nripsuta Saxena, Karen Huang, Evan DeFilippis, Goran Radanovic, David Parkes, and Yang Liu. 2019. “How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness.” In Proceedings of AI Ethics and Society (AIES) , Pp. 99-106. Download
Zhe Feng, David C. Parkes, and Haifeng Xu. 2019. “The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation”. Download
Sophie Hilgard, Nir Rosenfeld, Mahzarin R. Banaji, Jack Cao, and David C. Parkes. 2019. “Learning Representations by Humans, for Humans.” In NeurIPS Workshop on Human-Centric Machine Learning. Download
Goran Radanovic, Rati Devidze, David Parkes, and Adish Singla. 2019. “Learning to Collaborate in Markov Decision Processes.” In Proceedings of the 36th International Conference on Machine Learning (ICML'19), Pp. 5261-5270. Download
Paul Duetting, Zhe Feng, Noah Golowich, Harikrishna Narasimhan, David C. Parkes, and Sai Srivatsa Ravindranath. 2019. “Machine Learning for Optimal Economic Design.” In The Future of Economic Design, Pp. 495-515. Springer.
Zhe Feng, Okke Schrijvers, and Eric Sodomka. 2019. “Online Learning for Measuring Incentive Compatibility in Ad Auctions.” In Proceeding of The Web Conference 2019, Pp. 2729-2735. Download
Paul Duetting, Zhe Feng, Harikrishna Narasimham, David C. Parkes, and Sai S. Ravindranath. 2019. “Optimal Auctions through Deep Learning.” In Proceedings of the 36th International Conference on Machine Learning (ICML'19), Pp. 1706-1715. 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

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