no code implementations • 17 Feb 2024 • Sepanta Zeighami, Cyrus Shahabi
We show that statistical debiasing, although in some cases useful, often fails to improve accuracy.
no code implementations • 19 Jun 2023 • Sepanta Zeighami, Cyrus Shahabi
In this paper, we significantly strengthen this result, showing that under mild assumptions on data distribution, and the same space complexity as non-learned methods, learned indexes can answer queries in $O(\log\log n)$ expected query time.
1 code implementation • 14 Dec 2020 • Sirisha Rambhatla, Sepanta Zeighami, Kameron Shahabi, Cyrus Shahabi, Yan Liu
As countries look towards re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging.
no code implementations • 25 Sep 2019 • Zac Wellmer, Sepanta Zeighami, James Kwok
However, decision-time planning with implicit dynamics models in continuous action space has proven to be a difficult problem.
Model-based Reinforcement Learning Policy Gradient Methods +3
3 code implementations • 18 Oct 2018 • Sepanta Zeighami, Raymong Chi-Wing Wong
This problem takes into account the probability distribution of the users and considers the satisfaction (ratio) of all users, which is more reasonable in practice, compared with the existing studies that only consider the worst-case satisfaction (ratio) of the users, which may not reflect the whole population and is not useful in some applications.