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ORCIDZhe Feng 0004
Person information
- unicode name: 冯哲
- affiliation: Harvard University, School of Engineering and Applied Sciences, Cambridge, MA, USA
- affiliation (former): Shanghai Jiao Tong University, Department of Computer Science, China
Other persons with the same name
- Zhe Feng — disambiguation page
- Zhe Feng 0001 — Fudan University, Shanghai, China
- Zhe Feng 0002 — University of California, Los Angeles, USA
- Zhe Feng 0003 — Robert Bosch Research and Technology Center, Palo Alto, CA, USA
- Zhe Feng 0005 — University of Colorado Boulder, Boulder, CO, USA
- Zhe Feng 0006 — Nantong University, Nantong, China
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Journal Articles
- 2024
[j4]Paul Dütting
, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, Sai Srivatsa Ravindranath:
Optimal Auctions through Deep Learning: Advances in Differentiable Economics. J. ACM 71(1): 5:1-5:53 (2024)- 2021
- [j3]Paul Dütting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, Sai Srivatsa Ravindranath:
Optimal auctions through deep learning. Commun. ACM 64(8): 109-116 (2021) - [j2]Xiaotie Deng, Jack R. Edmonds, Zhe Feng, Zhengyang Liu, Qi Qi, Zeying Xu:
Understanding PPA-completeness. J. Comput. Syst. Sci. 115: 146-168 (2021) - 2018
- [j1]Zhe Feng, Jinglai Li:
An Adaptive Independence Sampler MCMC Algorithm for Bayesian Inferences of Functions. SIAM J. Sci. Comput. 40(3) (2018)
Conference and Workshop Papers
- 2024
- [c17]Yang Cai, Zhe Feng, Christopher Liaw, Aranyak Mehta, Grigoris Velegkas:
User Response in Ad Auctions: An MDP Formulation of Long-term Revenue Optimization. WWW 2024: 111-122 - 2023
- [c16]Chaoqi Wang, Ziyu Ye, Zhe Feng, Ashwinkumar Badanidiyuru Varadaraja, Haifeng Xu:
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts. NeurIPS 2023 - [c15]Ashwinkumar Badanidiyuru, Zhe Feng, Guru Guruganesh:
Learning to Bid in Contextual First Price Auctions✱. WWW 2023: 3489-3497 - [c14]Zhe Feng, Swati Padmanabhan, Di Wang:
Online Bidding Algorithms for Return-on-Spend Constrained Advertisers✱. WWW 2023: 3550-3560 - 2022
- [c13]Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan, Manzil Zaheer, Xiaotie Deng:
A Context-Integrated Transformer-Based Neural Network for Auction Design. ICML 2022: 5609-5626 - [c12]Ashwinkumar Badanidiyuru Varadaraja, Zhe Feng, Tianxi Li, Haifeng Xu:
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards. NeurIPS 2022 - [c11]Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu:
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning. EC 2022: 471-472 - 2021
- [c10]Zhe Feng, Guru Guruganesh, Christopher Liaw, Aranyak Mehta, Abhishek Sethi:
Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions. AAAI 2021: 5399-5406 - [c9]Zhe Feng, Sébastien Lahaie, Jon Schneider, Jinchao Ye:
Reserve Price Optimization for First Price Auctions in Display Advertising. ICML 2021: 3230-3239 - [c8]Vincent Conitzer, Zhe Feng, David C. Parkes, Eric Sodomka:
Welfare-Preserving ε-BIC to BIC Transformation with Negligible Revenue Loss. WINE 2021: 76-94 - 2020
- [c7]Zhe Feng, David C. Parkes, Haifeng Xu:
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation. ICML 2020: 3092-3101 - 2019
- [c6]Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, Sai Srivatsa Ravindranath:
Optimal Auctions through Deep Learning. ICML 2019: 1706-1715 - [c5]Zhe Feng, Okke Schrijvers, Eric Sodomka:
Online Learning for Measuring Incentive Compatibility in Ad Auctions? WWW 2019: 2729-2735 - 2018
- [c4]Zhe Feng, Harikrishna Narasimhan, David C. Parkes:
Deep Learning for Revenue-Optimal Auctions with Budgets. AAMAS 2018: 354-362 - [c3]Zhe Feng, Chara Podimata, Vasilis Syrgkanis:
Learning to Bid Without Knowing your Value. EC 2018: 505-522 - 2016
- [c2]Xiaotie Deng, Jack R. Edmonds, Zhe Feng, Zhengyang Liu, Qi Qi, Zeying Xu:
Understanding PPA-Completeness. CCC 2016: 23:1-23:25 - [c1]Xiaotie Deng, Zhe Feng, Christos H. Papadimitriou:
Power-Law Distributions in a Two-Sided Market and Net Neutrality. WINE 2016: 59-72
Informal and Other Publications
- 2024
- [i21]Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang:
Auctions with LLM Summaries. CoRR abs/2404.08126 (2024) - 2023
- [i20]Yang Cai, Zhe Feng, Christopher Liaw, Aranyak Mehta:
User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization. CoRR abs/2302.08108 (2023) - [i19]Chaoqi Wang, Ziyu Ye, Zhe Feng, Ashwinkumar Badanidiyuru, Haifeng Xu:
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts. CoRR abs/2309.13896 (2023) - [i18]Jiahao Zhang, Tao Lin, Weiqiang Zheng, Zhe Feng, Yifeng Teng, Xiaotie Deng:
Learning Thresholds with Latent Values and Censored Feedback. CoRR abs/2312.04653 (2023) - 2022
- [i17]Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan, Manzil Zaheer, Xiaotie Deng:
A Context-Integrated Transformer-Based Neural Network for Auction Design. CoRR abs/2201.12489 (2022) - [i16]Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu:
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning. CoRR abs/2202.10678 (2022) - [i15]Ashwinkumar Badanidiyuru, Zhe Feng, Tianxi Li, Haifeng Xu:
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards. CoRR abs/2206.01293 (2022) - [i14]Zhe Feng, Swati Padmanabhan, Di Wang:
Online Bidding Algorithms for Return-on-Spend Constrained Advertisers. CoRR abs/2208.13713 (2022) - 2021
- [i13]Sai Srivatsa Ravindranath, Zhe Feng, Shira Li, Jonathan Ma, Scott Duke Kominers, David C. Parkes:
Deep Learning for Two-Sided Matching. CoRR abs/2107.03427 (2021) - [i12]Zhe Feng, Sébastien Lahaie:
Robust Clearing Price Mechanisms for Reserve Price Optimization. CoRR abs/2107.04638 (2021) - [i11]Ashwinkumar Badanidiyuru, Zhe Feng, Guru Guruganesh:
Learning to Bid in Contextual First Price Auctions. CoRR abs/2109.03173 (2021) - 2020
- [i10]Zhe Feng, Sébastien Lahaie, Jon Schneider, Jinchao Ye:
Reserve Price Optimization for First Price Auctions. CoRR abs/2006.06519 (2020) - [i9]Vincent Conitzer, Zhe Feng, David C. Parkes, Eric Sodomka:
Welfare-Preserving ε-BIC to BIC Transformation with Negligible Revenue Loss. CoRR abs/2007.09579 (2020) - [i8]Zhe Feng, Guru Guruganesh, Christopher Liaw, Aranyak Mehta, Abhishek Sethi:
Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions. CoRR abs/2009.06136 (2020) - 2019
- [i7]Zhe Feng, Okke Schrijvers, Eric Sodomka:
Online Learning for Measuring Incentive Compatibility in Ad Auctions. CoRR abs/1901.06808 (2019) - [i6]Zhe Feng, David C. Parkes, Haifeng Xu:
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation. CoRR abs/1906.01528 (2019) - 2017
- [i5]Paul Dütting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes:
Optimal Auctions through Deep Learning. CoRR abs/1706.03459 (2017) - [i4]Zhe Feng, Chara Podimata, Vasilis Syrgkanis:
Learning to Bid Without Knowing your Value. CoRR abs/1711.01333 (2017) - [i3]Xiaotie Deng, Zhe Feng, Rucha Kulkarni:
Octahedral Tucker is PPA-Complete. Electron. Colloquium Comput. Complex. TR17 (2017) - 2016
- [i2]Xiaotie Deng, Zhe Feng, Christos H. Papadimitriou:
Power-Law Distributions in a Two-sided Market and Net Neutrality. CoRR abs/1610.04809 (2016) - 2015
- [i1]Xiaotie Deng, Zhe Feng, Zhengyang Liu, Qi Qi:
Understanding PPA-Completeness. Electron. Colloquium Comput. Complex. TR15 (2015)
Coauthor Index
aka: Ashwinkumar Badanidiyuru Varadaraja
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