11 citations · 29 across the 17 of their papers we have counts for
5 papers · 1 filter
C-OPH: Improving the Accuracy of One Permutation Hashing (OPH) with Circulant Permutations
Xiaoyun Li, Ping Li
Minwise hashing (MinHash) is a classical method for efficiently estimating the Jaccrad similarity in massive binary (0/1) data. To generate hash values for each data vector, th…
Layer-wise and Dimension-wise Locally Adaptive Federated Learning
Belhal Karimi, Ping Li, Xiaoyun Li
In the emerging paradigm of Federated Learning (FL), large amount of clients such as mobile devices are used to train possibly high-dimensional models on their respective data. Com…
C-MinHash: Practically Reducing Two Permutations to Just One
Xiaoyun Li, Ping Li
Traditional minwise hashing (MinHash) requires applying independent permutations to estimate the Jaccard similarity in massive binary (0/1) data, where can be (e.g.,) 1024…
C-MinHash: Rigorously Reducing Permutations to Two
Xiaoyun Li, Ping Li
Minwise hashing (MinHash) is an important and practical algorithm for generating random hashes to approximate the Jaccard (resemblance) similarity in massive binary (0/1) data. The…
On the Convergence of Decentralized Adaptive Gradient Methods
Xiangyi Chen, Belhal Karimi, Weijie Zhao +1
Adaptive gradient methods including Adam, AdaGrad, and their variants have been very successful for training deep learning models, such as neural networks. Meanwhile, given the nee…