Publications

Sarah Keren, Gopal K. Vashishtha, and David C. Parkes. 2019. “Reinforcement Learning Design.” In ICAPS 2019 Workshop on Reasoning about Actions and Processes: Highlights of Recent Advances (RAC-ICAPS 2019).

Duncan Rheingans-Yoo, Scott Duke Kominers, Hongyao Ma, and David C. Parkes. 2019. “Ridesharing with Driver Location Preferences.” In Proceedings of the 28th Int. Joint Conf. on Artificial Intelligence, (IJCAI 2019), Pp. 557-564. Download

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

David C. Parkes. 1/2018. “Technical perspective: Moving spectrum.” Communications of the ACM, 61, 1, Pp. 96. Download

Eric Balkanski and Yaron Singer. 2018. “Approximation Guarantees for Adaptive Sampling.” In Proceedings of the 35th International Conference on Machine Learning (ICML 2018).

Dimitris Fotakis, Kyriakos Lotidis, and Chara Podimata. 2018. “A Bridge between Liquid and Social Welfare in Combinatorial Auctions with Submodular Bidders”. Download

Lily Hu. 2018. “Justice Beyond Utility in Artificial Intelligence.” Artificial Intelligence, Ethics, and Society. (AIES 2018). Download

Alexander Spangher and Berk Ustun. 2018. “Actionable Recourse in Linear Classification.” In 5th Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2018). Download

Lily Hu, Bryan Wilder, Amulya Yadav, Eric Rice, and Milind Tambe. 2018. “Activating the 'Breakfast Club': Modeling Influence Spread in Natural-World Social Networks.” In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems. (AAMAS 2018). 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

Eric Balkanski and Yaron Singer. 2018. “The Adaptive Complexity of Maximizing a Submodular Function.” In Proceedings of the ACM Symposium on the Theory of Computation (STOC) 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

Goran Radanovic, Adish Singla, Andreas Krause, and Boi Faltings. 2018. “Information Gathering with Peers: Submodular Optimization with Peer-Prediction Constraints.” In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18). Download

Dimitris Kalimeris, Karthik Subbian, Udi Weinsberg, and Yaron Singer. 2018. “Learning Diffusion using Hyperparameters.” In Proceedings of the 35th International Conference on Machine Learning (ICML 2018).

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