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Abstract. While the AI alignment problem—the notion that machine and human values may not be aligned—has arisen as an impetus for regulation, what is less recognized is that hurried calls to regulate create their own regulatory alignment problem, where proposals may distract, fail, or backfire. In this brief, we shed light on this ...
Liked by Neel Guha. Experience: Laserlike Inc · Education: Carnegie Mellon University · Location: Los Altos · 500+ connections on LinkedIn. View Neel Guha’s profile on LinkedIn, a ...
- Laserlike Inc
Jan 17, 2024 · The authors review challenges arising in malpractice litigation related to software errors to inform health care organizations and physicians about liability risk from AI adoption and about strateg...
Amanda Coston, Neel Guha, Lisa Lu, Derek Ouyang, Alexandra Chouldechova, and Daniel E. Ho [17] Machine Learning for AC Optimal Power Flow Climate Change Workshop at the International Conference on Machine Learning (2019) (Honorable Mention for Best Paper) Neel Guha, Zhecheng Wang, Matt Wytock, and Arun Majumdar [18] One-Shot Federated Learning
Neel Guha Email:nguha@cs.stanford.edu Website:neelguha.com Education Ph.D.ComputerScience,StanfordUniversity,2021- Advisor: ChristopherRé J.D.StanfordLawSchool,2020-
We present one-shot federated learning, where a central server learns a global model over a network of federated devices in a single round of communication. Our approach - drawing on ensemble learning and knowledge aggregation - achieves an average relative gain of 51.5% in AUC over local baselines and comes within 90.1% of the (unattainable) global ideal. We discuss these methods and identify several promising directions of future work.
