Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Jeong, Haewon"'
Diffusion models have achieved tremendous improvements in generative modeling for images, enabling high-quality generation that is indistinguishable by humans from real images. The qualities of images have reached a threshold at which we can reuse sy
Externí odkaz:
http://arxiv.org/abs/2407.17493
Differentially private distributed mean estimation (DP-DME) is a fundamental building block in privacy-preserving federated learning, where a central server estimates the mean of $d$-dimensional vectors held by $n$ users while ensuring $(\epsilon,\de
Externí odkaz:
http://arxiv.org/abs/2407.03289
Parallel tensor network contraction algorithms have emerged as the pivotal benchmarks for assessing the classical limits of computation, exemplified by Google's demonstration of quantum supremacy through random circuit sampling. However, the massive
Externí odkaz:
http://arxiv.org/abs/2405.13946
Autor:
Albalak, Alon, Elazar, Yanai, Xie, Sang Michael, Longpre, Shayne, Lambert, Nathan, Wang, Xinyi, Muennighoff, Niklas, Hou, Bairu, Pan, Liangming, Jeong, Haewon, Raffel, Colin, Chang, Shiyu, Hashimoto, Tatsunori, Wang, William Yang
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. However, naively training a model on all available data may not be optimal (or feasible), as the qualit
Externí odkaz:
http://arxiv.org/abs/2402.16827
QR decomposition is an essential operation for solving linear equations and obtaining least-squares solutions. In high-performance computing systems, large-scale parallel QR decomposition often faces node faults. We address this issue by proposing a
Externí odkaz:
http://arxiv.org/abs/2311.11943
We consider the problem of private distributed multi-party multiplication. It is well-established that Shamir secret-sharing coding strategies can enable perfect information-theoretic privacy in distributed computation via the celebrated algorithm of
Externí odkaz:
http://arxiv.org/abs/2309.16105
Autor:
Alghamdi, Wael, Hsu, Hsiang, Jeong, Haewon, Wang, Hao, Michalak, P. Winston, Asoodeh, Shahab, Calmon, Flavio P.
We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of "projecting" a pre-trained (and potentially unfair) classifier onto the set of models that satisfy target
Externí odkaz:
http://arxiv.org/abs/2206.07801
Autor:
Yoo, Jin, Hwang, Jinsu, Choi, Jiyun, Ramalingam, Mahesh, Jeong, Haewon, Jang, Sujeong, Jeong, Han-Seong, Kim, Daeyeol
Publikováno v:
In Heliyon 15 September 2024 10(17)
We investigate the fairness concerns of training a machine learning model using data with missing values. Even though there are a number of fairness intervention methods in the literature, most of them require a complete training set as input. In pra
Externí odkaz:
http://arxiv.org/abs/2109.10431
We study coded distributed matrix multiplication from an approximate recovery viewpoint. We consider a system of $P$ computation nodes where each node stores $1/m$ of each multiplicand via linear encoding. Our main result shows that the matrix produc
Externí odkaz:
http://arxiv.org/abs/2105.01973