14 citations · 30 across the 7 of their papers we have counts for
9 papers · 1 filter
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…
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…
A High Probability Analysis of Adaptive SGD with Momentum
Xiaoyu Li, Francesco Orabona
Stochastic Gradient Descent (SGD) and its variants are the most used algorithms in machine learning applications. In particular, SGD with adaptive learning rates and momentum is th…
IVFS: Simple and Efficient Feature Selection for High Dimensional Topology Preservation
Xiaoyun Li, Chengxi Wu, Ping Li
Feature selection is an important tool to deal with high dimensional data. In unsupervised case, many popular algorithms aim at maintaining the structure of the original data. In t…