1 code implementation • 26 Feb 2024 • Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, Colin Raffel, Shiyu Chang, Tatsunori Hashimoto, William Yang Wang
A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training.
no code implementations • 28 Sep 2023 • Viveck R. Cadambe, Ateet Devulapalli, Haewon Jeong, Flavio P. Calmon
We consider the problem of private distributed multi-party multiplication.
1 code implementation • 15 Jun 2022 • Wael Alghamdi, Hsiang Hsu, Haewon Jeong, Hao Wang, P. Winston Michalak, Shahab Asoodeh, Flavio P. Calmon
We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks.
no code implementations • 21 Sep 2021 • Haewon Jeong, Hao Wang, Flavio P. Calmon
We investigate the fairness concerns of training a machine learning model using data with missing values.
no code implementations • 27 Nov 2018 • Sanghamitra Dutta, Ziqian Bai, Haewon Jeong, Tze Meng Low, Pulkit Grover
First, we propose a novel coded matrix multiplication technique called Generalized PolyDot codes that advances on existing methods for coded matrix multiplication under storage and communication constraints.
3 code implementations • 31 Jan 2018 • Sanghamitra Dutta, Mohammad Fahim, Farzin Haddadpour, Haewon Jeong, Viveck Cadambe, Pulkit Grover
We provide novel coded computation strategies for distributed matrix-matrix products that outperform the recent "Polynomial code" constructions in recovery threshold, i. e., the required number of successful workers.
Information Theory Distributed, Parallel, and Cluster Computing Information Theory