no code implementations • 16 Jun 2022 • Romain Cosson, Ali Jadbabaie, Anuran Makur, Amirhossein Reisizadeh, Devavrat Shah
When $r \ll p$, these complexities are smaller than the known complexities of $\mathcal{O}(p \log(1/\epsilon))$ and $\mathcal{O}(p/\epsilon^2)$ of {\gd} in the strongly convex and non-convex settings, respectively.
1 code implementation • 28 May 2022 • Qian Zhang, Anuran Makur, Kamyar Azizzadenesheli
In particular, given $n$ samples with $d$ basis functions, we show estimation error upper bounds of $\widetilde O(\sqrt{d/n})$ for fixed design, random design, and adversarial context cases.
no code implementations • 6 Jan 2022 • Ali Jadbabaie, Anuran Makur, Devavrat Shah
Under some assumptions on the loss function, e. g., strong convexity in parameter, $\eta$-H\"older smoothness in data, etc., we prove that the federated oracle complexity of FedLRGD scales like $\phi m(p/\epsilon)^{\Theta(d/\eta)}$ and that of FedAve scales like $\phi m(p/\epsilon)^{3/4}$ (neglecting sub-dominant factors), where $\phi\gg 1$ is a "communication-to-computation ratio," $p$ is the parameter dimension, and $d$ is the data dimension.
no code implementations • 22 Apr 2021 • Ali Jadbabaie, Anuran Makur, Elchanan Mossel, Rabih Salhab
At each time step, agents broadcast their declared opinions on a social network, which are governed by the agents' inherent opinions and social pressure.
no code implementations • NeurIPS 2020 • Ali Jadbabaie, Anuran Makur, Devavrat Shah
In this paper, we study the problem of learning the skill distribution of a population of agents from observations of pairwise games in a tournament.
no code implementations • 4 Nov 2020 • Ali Jadbabaie, Anuran Makur, Devavrat Shah
In contrast, we demonstrate that when the loss function is smooth in the data, we can learn the oracle at every iteration and beat the oracle complexities of both GD and SGD in important regimes.
no code implementations • 15 Jun 2020 • Ali Jadbabaie, Anuran Makur, Devavrat Shah
In this paper, we study the problem of learning the skill distribution of a population of agents from observations of pairwise games in a tournament.
no code implementations • 20 Nov 2019 • Shao-Lun Huang, Anuran Makur, Gregory W. Wornell, Lizhong Zheng
We consider the problem of identifying universal low-dimensional features from high-dimensional data for inference tasks in settings involving learning.
no code implementations • 10 Oct 2018 • David Qiu, Anuran Makur, Lizhong Zheng
In this paper, we present a local information theoretic approach to explicitly learn probabilistic clustering of a discrete random variable.