I am a CS PhD student at Stanford. I obtained a masters degree in CS from UIUC. My undergraduate major was Computational and Applied Mathematics at University of Chicago. I have a variety of research interests in machine learning. I’m particularly motivated by questions surrounding human incentives and the creation of human compatible AI. I’m also interested in imitation learning, generative models, and learning theory as well.


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Ongoing Projects

  1. Zachary Robertson. GPT4 is Slightly Helpful for Peer-Review Assistance: A Pilot Study. arXiv preprint arXiv:2307.05492, 2023
  2. Zachary Robertson and Sanmi Koyejo. No Bidding, No Regret: Pairwise-Feedback Mechanisms for Digital Goods and Data Auctions. arXiv preprint arXiv:2306.01860, 2023


  1. Zachary Robertson and Sanmi Koyejo. Layer-Wise Alignment is Conserved in Deep Neural Networks. ICML Workshop on Localized Learning (LLW), 2023.
  2. Boxiang Lyu, Zhe Feng, Zachary Robertson, and Sanmi Koyejo. Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions. ICML, 2023
  3. Zachary Robertson, Hantao Zhang, and Sanmi Koyejo. Cooperative inverse decision theory for uncertain preferences. AISTATS, 2023
  4. Zachary Robertson, Hantao Zhang, and Sanmi Koyejo. Probabilistic performance metric elicitation. 1st Workshop on Human and Machine Decisions (WHMD 2021) at NeurIPS 2021, 2022
  5. Zachary Robertson and Matthew Walter. Concurrent training improves the performance of behavioral cloning from observation. arXiv preprint arXiv:2008.01205, 2020
  6. Julian Stürmer, Andreas Seifahrt, Zachary Robertson, Christian Schwab, and Jacob L Bean. Echelle++, a fast generic spectrum simulator. Publications of the Astronomical Society of the Pacific, 131(996):024502, 2018