399 citations · 3.4k across the 95 of their papers we have counts for
11 papers · 1 filter
Variance Reduced methods for Non-convex Composition Optimization
Liu Liu, Ji Liu, Dacheng Tao
This paper explores the non-convex composition optimization in the form including inner and outer finite-sum functions with a large number of component functions. This problem aris…
Gradient Sparsification for Communication-Efficient Distributed Optimization
Jianqiao Wangni, Jialei Wang, Ji Liu +1
Modern large scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computational architectures. A key bottleneck is the co…
Duality-free Methods for Stochastic Composition Optimization
Liu Liu, Ji Liu, Dacheng Tao
We consider the composition optimization with two expected-value functions in the form of ,…
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian, Wei Zhang, Ce Zhang +1
Most commonly used distributed machine learning systems are either synchronous or centralized asynchronous. Synchronous algorithms like AllReduce-SGD perform poorly in a heterogene…
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads
Tian Li, Jie Zhong, Ji Liu +2
We present ease.ml, a declarative machine learning service platform we built to support more than ten research groups outside the computer science departments at ETH Zurich for the…
An Interactive Greedy Approach to Group Sparsity in High Dimensions
Wei Qian, Wending Li, Yasuhiro Sogawa +3
Sparsity learning with known grouping structure has received considerable attention due to wide modern applications in high-dimensional data analysis. Although advantages of using…
Lifelong Metric Learning
Gan Sun, Yang Cong, Ji Liu +1
The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider lifelong learning problem to mimic "human l…
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian, Ce Zhang, Huan Zhang +3
Most distributed machine learning systems nowadays, including TensorFlow and CNTK, are built in a centralized fashion. One bottleneck of centralized algorithms lies on high communi…
Asynchronous Parallel Empirical Variance Guided Algorithms for the Thresholding Bandit Problem
Jie Zhong, Yijun Huang, Ji Liu
This paper considers the multi-armed thresholding bandit problem -- identifying all arms whose expected rewards are above a predefined threshold via as few pulls (or rounds) as pos…
On The Projection Operator to A Three-view Cardinality Constrained Set
Haichuan Yang, Shupeng Gui, Chuyang Ke +3
The cardinality constraint is an intrinsic way to restrict the solution structure in many domains, for example, sparse learning, feature selection, and compressed sensing. To solve…
Negative-Unlabeled Tensor Factorization for Location Category Inference from Highly Inaccurate Mobility Data
Jinfeng Yi, Qi Lei, Wesley Gifford +2
Identifying significant location categories visited by mobile users is the key to a variety of applications. This is an extremely challenging task due to the possible deviation bet…