32 citations · 80 across the 15 of their papers we have counts for
6 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…
Toward Communication Efficient Adaptive Gradient Method
Xiangyi Chen, Xiaoyun Li, Ping Li
In recent years, distributed optimization is proven to be an effective approach to accelerate training of large scale machine learning models such as deep neural networks. With the…
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…
Quantization Algorithms for Random Fourier Features
Xiaoyun Li, Ping Li
The method of random projection (RP) is the standard technique in machine learning and many other areas, for dimensionality reduction, approximate near neighbor search, compressed…
On the Last Iterate Convergence of Momentum Methods
Xiaoyu Li, Mingrui Liu, Francesco Orabona
SGD with Momentum (SGDM) is a widely used family of algorithms for large-scale optimization of machine learning problems. Yet, when optimizing generic convex functions, no advantag…