Publications

You can find my full publications on my Google Scholar profile.

Conference Papers.

2025+

• Online Bidding under RoS Constraints without Knowing the Value. (Alphabetical) Sushant Vijayan, Zhe Feng, Swati Padmanabhan, Karthikeyan Shanmugam, Arun Suggala and Di Wang. TheWebConf-25. arXiv.
• Deviate or Not: Learning Coalition Structures with Multiple-bit Observations in Games. Yixuan Even Xu, Zhe Feng and Fei Fang. AAAI-25. arXiv.

2024

• Auto-bidding and Auctions in Online Advertising: A Survey. Many Google Colleagues. SIGecom Exchanges 2024. arXiv.
• The Average-Value Allocation Problem. (Alphabetical) Kshipra Bhawalkar, Zhe Feng, Anupam Gupta, Aranyak Mehta, David Wajc, and Di Wang. APPROX/RANDOM-24 (Invited to the special issue of the journal Theory of Computing). arXiv.
• Auctions with LLM Summaries. (Alphabetical) Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta and Di Wang. KDD-24. arXiv.
• A Field Guide for Pacing Budget and ROS Constraints. (Alphabetical) Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang. ICML-24. arXiv.
• User Response in Ad Auctions: An MDP Formulation of Long-term Revenue Optimization. (Alphabetical) Yang Cai, Zhe Feng, Christopher Liaw, Aranyak Mehta and Grigoris Velegkas. TheWebConf-24. arXiv.
• Learning Thresholds with Latent Values and Censored Feedback. Jiahao Zhang, Tao Lin, Weiqiang Zheng, Zhe Feng, Yifeng Teng and Xiaotie Deng. ICLR-24. arXiv.

2023

• Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts. Chaoqi Wang, Ziyu Ye, Zhe Feng, Ashwinkumar Badanidiyuru and Haifeng Xu. NeurIPS-23 (Spotlight). arXiv.
• Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions. Boxiang Lyu, Zhe Feng, Zachary Robertson and Sanmi Koyejo. ICML-23. arXiv.
• Improved Online Learning Algorithms for CTR Prediction in Ad Auctions. (Alphabetical) Zhe Feng, Christopher Liaw and Zixin Zhou. ICML-23. Conference Version.
• Learning to Bid in Contextual First Price Auctions. (Alphabetical) Ashwinkumar Badanidiyuru, Zhe Feng and Guru Guruganesh. TheWebConf-23. arXiv.
• Online Bidding Algorithms for Return-on-Spend Constrained Advertisers. (Alphabetical) Zhe Feng, Swati Padmanabhan, Di Wang. TheWebConf-23. arXiv.

2022

• Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards. (Alphabetical) Ashwinkumar Badanidiyuru Varadaraja, Zhe Feng, Tianxi Li and Haifeng Xu. NeurIPS-22. arXiv.
• A Context-Integrated Transformer-Based Neural Network for Auction Design. Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan, Manzil Zaheer, and Xiaotie Deng. ICML-22. arXiv.
• Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning. Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu. EC-22. arXiv.

2021 and before

• Welfare-Preserving $\varepsilon$-BIC to BIC Transformation with Negligible Revenue Loss. (Alphabetical) Vincent Conitzer, Zhe Feng, David C. Parkes, and Eric Sodomka. WINE-21. arXiv.
• Reserve Price Optimization for First Price Auctions in Display Advertising. Zhe Feng, Sebastien Lahaie, Jon Schneider, and Jinchao Ye. ICML-21 (Long Oral). arXiv.
• Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions. (Alphabetical) Zhe Feng, Guru Guruganesh, Christopher Liaw, Aranyak Mehta, and Abhishek Sethi. AAAI-21. arXiv.
• The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation. (Alphabetical) Zhe Feng, David C. Parkes, and Haifeng Xu. ICML-20. arXiv.
• Optimal Auctions through Deep Learning. (Alphabetical) Paul Dütting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, and Sai S. Ravindranath. ICML-19 (Long Oral). arXiv.
• Online Learning for Measuring Incentive Compatibility in Ad Auctions. Zhe Feng, Okke Schrijvers, and Eric Sodomka. TheWebConf-19. arXiv.
• Deep Learning for Revenue-Optimal Auctions with Budgets. Zhe Feng, Harikrishna Narasimhan, and David C. Parkes. AAMAS-18. Conference Version.
• Learning to Bid Without Knowing your Value. (Alphabetical) Zhe Feng, Chara Podimata, and Vasilis Syrgkanis. EC-18. arXiv.
• Power-Law Distributions in a Two-sided Market and Net Neutrality. (Alphabetical) Xiaotie Deng, Zhe Feng, and Christos H. Papadimitriou. WINE-16. arXiv.
• Understanding PPA-Completeness. (Alphabetical) Xiaotie Deng, Jack R. Edmonds, Zhe Feng, Zhengyang Liu, Qi Qi, and Zeying Xu. CCC-16. Conference Version.

Journal Papers and Book Chapters.

• Optimal Auctions through Deep Learning: Advances in Differentiable Economics. (Alphabetical) Paul Dütting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, and Sai S. Ravindranath. Journal of the ACM, Volume 71, Issue 1, Article No.:5, pp 1-53, supercedes the previous ICML-19 and CACM versions.
• Machine Learning for Matching Markets. (Alphabetical) Zhe Feng, David C. Parkes, and Sai S. Ravindranath. Online and Matching-Based Market Design, Chapter 28, pp. 591-613, May 2023.
• Optimal Auctions through Deep Learning. (Alphabetical) Paul Dütting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, and Sai S. Ravindranath. Communications of the ACM, Volume 64, Issue 8, August 2021.
• Understanding PPA-Completeness. (Alphabetical) Xiaotie Deng, Jack R. Edmonds, Zhe Feng, Zhengyang Liu, Qi Qi, and Zeying Xu. Journal of Computer and System Sciences, 2021, supercedes the previous CCC-16 version.
• Machine Learning for Optimal Economic Design. (Alphabetical) Paul Dütting, Zhe Feng, Noah Golowich, Harikrishna Narasimhan, David C. Parkes, and Sai S. Ravindranath. The Future of Economic Design, pp 495-515, 2019.
• An Adaptive Independence Sampler MCMC Algorithm for Bayesian Inferences of Functions. Zhe Feng and Jinglai Li. SIAM Journal on Scientific Computing, 40(3), pp 1301-1321, 2018. arXiv.