14 citations · 30 across the 7 of their papers we have counts for
11 papers
On Distributed Adaptive Optimization with Gradient Compression
Xiaoyun Li, Belhal Karimi, Ping Li
We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm. Gradient compression with error feedback is applied to reduce 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…
FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching
Farzin Haddadpour, Belhal Karimi, Ping Li +1
Communication complexity and privacy are the two key challenges in Federated Learning where the goal is to perform a distributed learning through a large volume of devices. In this…