Zhe Feng, David C. Parkes, and Haifeng Xu. Working Paper. “The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation”. ArXiv

Xiaotie Deng, Zhe Feng, and Rucha Kulkarni. In Preparation. Octahedral Tucker is PPA-complete. ECCC-TR17-118

Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, and Sai Srivatsa Ravindranath. 6/2019. “Optimal Auctions through Deep Learning.” 36th International Conference on Machine Learning (ICML 2019). Long Talk. code ArXiv

Zhe Feng, Okke Schrijvers, and Eric Sodomka. 5/2019. “Online Learning for Measuring Incentive Compatibility in Ad Auctions.” The Web Conference 2019 (former WWW'19). ArXiv

Zhe Feng, Harikrishna Narasimhan, and David C. Parkes. 7/2018. “Deep Learning for Revenue-Optimal Auctions with budgets.” 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018), Full paper. PDF

Zhe Feng, Chara Podimata, and Vasilis Syrgkanis. 6/2018. “Learning to Bid Without Knowing your Value.” 19th ACM Conference on Economics and Computation (EC 2018) . ArXiv

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. A1301–A1321. ArXiv

Xiaotie Deng, Zhe Feng, and Christos H. Papadimitriou. 12/2016. “Power-Law Distributions in a Two-sided Market and Net Neutrality.” The Proceedings of 12th Conference on Web and Internet Economics (WINE 2016) 10123, Pp. 59-72. Montreal, Canada. ArXiv PPT

Xiaotie Deng, Jack R. Edmonds, Zhe Feng, Zhengyang Liu, Qi Qi, and Zeying Xu. 6/2016. “Understanding PPA-Completeness.” The Proceedings of 31st Computational Complexity Conference (CCC 2016) 50, Pp. 23:1--23:25. Tokyo, Japan. PDF