Zixin Zhou

 

My name is Zixin Zhou, and I also go by Jack. I am currently in my third year as a Computer Science PhD student at Stanford University, under the esteemed guidance of Aviad Rubinstein and Adam Bouland. Before embarking on this journey, I achieved my Bachelor's degree from Turing Class, Peking University, where I had the privilege of being mentored by Xiaotie Deng. I am also pleased to have worked with Ariel Procaccia and Matt Weinberg during my undergraduate years.

I have a broad interest in theoretical computer science with particular interests in quantum computing and algorithmic game theory.

Contact: jackzhou [at] stanford [dot] edu

Papers

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• Quantum Communication Complexity of Classical Auctions. (arXiv)

  Aviad Rubinstein, and Zixin Zhou.

  Working paper (2023) .

• Public-key pseudoentanglement and the hardness of learning ground state entanglement structure. (arXiv)

  Adam Bouland, Bill Fefferman, Soumik Ghosh, Tony Metger, Umesh Vazirani, Chenyi Zhang, and Zixin Zhou.

  To appear as a contributed talk at Quantum Information Processing (QIP), 2024 .

• Improved Online Learning Algorithms for CTR Prediction in Ad Auctions. (pdf)

  Zhe Feng, Christopher Liaw, and Zixin Zhou.

  Proceedings of the 40th International Conference on Machine Learning (ICML), 2023.

• Quantum Pseudoentanglement. (arXiv)

  Scott Aaronson, Adam Bouland, Bill Fefferman, Soumik Ghosh, Umesh Vazirani, Chenyi Zhang, and Zixin Zhou.

  Presented as a contributed talk at Quantum Information Processing (QIP), 2023.
  To appear in Proceedings of the 15th Innovations in Theoretical Computer Science (ITCS), 2024.

• Optimal Multi-Dimensional Mechanisms are not Locally-Implementable. (arXiv)

  S. Matthew Weinberg, and Zixin Zhou.

  In Proceedings of the 23rd ACM Conference on Economics and Computation (EC), 2022.

• Explainable Voting. (pdf)

  Dominik Peters, Ariel D. Procaccia, Alexandros Psomas, and Zixin Zhou.

  In Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS), 2020.

• (Locally) Differentially Private Combinatorial Semi-Bandits. (arXiv)

  Xiaoyu Chen, Kai Zheng, Zixin Zhou, Yunchang Yang, Wei Chen, and Liwei Wang.

  In Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.

• An Improved Incentive Ratio of the Resource Sharing on Cycles. (pdf)

  Yukun Cheng and Zixin Zhou.

  In Journal of the Operations Research Society of China 7:409–427, 2019.

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Last update: 2023-12