1 code implementation • 6 Jun 2024 • Rajarshi Saha, Mohamed Seif, Michal Yemini, Andrea J. Goldsmith, H. Vincent Poor

We consider the problem of privately estimating the mean of vectors distributed across different nodes of an unreliable wireless network, where communications between nodes can fail intermittently.

1 code implementation • 29 May 2024 • Rajarshi Saha, Naomi Sagan, Varun Srivastava, Andrea J. Goldsmith, Mert Pilanci

$\rm CALDERA$ obtains this decomposition by formulating it as an optimization problem $\min_{\mathbf{Q},\mathbf{L},\mathbf{R}}\lVert(\mathbf{Q} + \mathbf{L}\mathbf{R} - \mathbf{W})\mathbf{X}^\top\rVert_{\rm F}^2$, where $\mathbf{X}$ is the calibration data, and $\mathbf{Q}, \mathbf{L}, \mathbf{R}$ are constrained to be representable using low-precision formats.

1 code implementation • NeurIPS 2023 • Rajarshi Saha, Varun Srivastava, Mert Pilanci

We propose an algorithm that exploits this structure to obtain a low rank decomposition of any matrix $\mathbf{A}$ as $\mathbf{A} \approx \mathbf{L}\mathbf{R}$, where $\mathbf{L}$ and $\mathbf{R}$ are the low rank factors.

no code implementations • 28 Feb 2023 • Rajarshi Saha, Mohamed Seif, Michal Yemini, Andrea J. Goldsmith, H. Vincent Poor

This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central server.

no code implementations • 23 May 2022 • Michal Yemini, Rajarshi Saha, Emre Ozfatura, Deniz Gündüz, Andrea J. Goldsmith

We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS).

no code implementations • 24 Feb 2022 • Michal Yemini, Rajarshi Saha, Emre Ozfatura, Deniz Gündüz, Andrea J. Goldsmith

Intermittent connectivity of clients to the parameter server (PS) is a major bottleneck in federated edge learning frameworks.

no code implementations • 23 Feb 2022 • Rajarshi Saha, Mert Pilanci, Andrea J. Goldsmith

We derive an information-theoretic lower bound for the minimax risk under this setting and propose a matching upper bound using randomized embedding-based algorithms which is tight up to constant factors.

no code implementations • 2 Oct 2021 • Erdem Biyik, Anusha Lalitha, Rajarshi Saha, Andrea Goldsmith, Dorsa Sadigh

Our results show that the proposed partner-aware strategy outperforms other known methods, and our human subject studies suggest humans prefer to collaborate with AI agents implementing our partner-aware strategy.

1 code implementation • 13 Mar 2021 • Rajarshi Saha, Mert Pilanci, Andrea J. Goldsmith

As a consequence, quantizing these embeddings followed by an inverse transform to the original space yields a source coding method with optimal covering efficiency while utilizing just $R$-bits per dimension.

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