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ORCIDZhe Feng 0004
Person information
- unicode name: 冯哲
- affiliation: Google Research, Mountain View, CA, USA
- 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
- Zhe Feng 0007
![[0000-0002-7369-676]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-7369-676")
— China University of Geosciences, School of Land Science and Technology, Beijing, China
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2020 – today
- 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)- [c21]Kshipra Bhawalkar, Zhe Feng, Anupam Gupta, Aranyak Mehta, David Wajc, Di Wang:
The Average-Value Allocation Problem. APPROX/RANDOM 2024: 13:1-13:23 - [c20]Jiahao Zhang, Tao Lin, Weiqiang Zheng, Zhe Feng, Yifeng Teng, Xiaotie Deng:
Learning Thresholds with Latent Values and Censored Feedback. ICLR 2024 - [c19]Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang:
A Field Guide for Pacing Budget and ROS Constraints. ICML 2024 - [c18]Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang:
Auctions with LLM Summaries. KDD 2024: 713-722 - [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 - [i26]Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang:
Auctions with LLM Summaries. CoRR abs/2404.08126 (2024) - [i25]Seyed A. Esmaeili, Kshipra Bhawalkar, Zhe Feng, Di Wang, Haifeng Xu:
How to Strategize Human Content Creation in the Era of GenAI? CoRR abs/2406.05187 (2024) - [i24]Sai Srivatsa Ravindranath, Zhe Feng, Di Wang, Manzil Zaheer, Aranyak Mehta, David C. Parkes:
Deep Reinforcement Learning for Sequential Combinatorial Auctions. CoRR abs/2407.08022 (2024) - [i23]Kshipra Bhawalkar, Zhe Feng, Anupam Gupta, Aranyak Mehta, David Wajc, Di Wang:
The Average-Value Allocation Problem. CoRR abs/2407.10401 (2024) - [i22]Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro, Kshipra Bhawalkar, Yuan Deng, Zhe Feng, Gagan Goel, Christopher Liaw, Haihao Lu, Mohammad Mahdian, Jieming Mao, Aranyak Mehta, Vahab Mirrokni, Renato Paes Leme, Andrés Perlroth, Georgios Piliouras, Jon Schneider, Ariel Schvartzman, Balasubramanian Sivan, Kelly Spendlove, Yifeng Teng, Di Wang, Hanrui Zhang, Mingfei Zhao, Wennan Zhu, Song Zuo:
Auto-bidding and Auctions in Online Advertising: A Survey. CoRR abs/2408.07685 (2024) - 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 - [i21]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) - [i20]Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang:
Joint Feedback Loop for Spend and Return-On-Spend Constraints. CoRR abs/2302.08530 (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
- [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 - [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
- [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) - [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 - [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
- [c7]Zhe Feng, David C. Parkes, Haifeng Xu:
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation. ICML 2020: 3092-3101 - [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)
2010 – 2019
- 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 - [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) - 2018
- [j1]Zhe Feng, Jinglai Li:
An Adaptive Independence Sampler MCMC Algorithm for Bayesian Inferences of Functions. SIAM J. Sci. Comput. 40(3) (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 - 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
- [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 - [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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