60 citations · 541 across the 61 of their papers we have counts for
16 papers · 1 filter
CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning
Haoteng Tang, Guixiang Ma, Lifang He +2
Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for gr…
Improved Bilevel Model: Fast and Optimal Algorithm with Theoretical Guarantee
Junyi Li, Bin Gu, Heng Huang
Due to the hierarchical structure of many machine learning problems, bilevel programming is becoming more and more important recently, however, the complicated correlation between…
Periodic Stochastic Gradient Descent with Momentum for Decentralized Training
Hongchang Gao, Heng Huang
Decentralized training has been actively studied in recent years. Although a wide variety of methods have been proposed, yet the decentralized momentum SGD method is still underexp…
Adaptive Serverless Learning
Hongchang Gao, Heng Huang
With the emergence of distributed data, training machine learning models in the serverless manner has attracted increasing attention in recent years. Numerous training approaches h…
Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning
Bin Gu, An Xu, Zhouyuan Huo +2
The privacy-preserving federated learning for vertically partitioned data has shown promising results as the solution of the emerging multi-party joint modeling application, in whi…
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization
Feihu Huang, Songcan Chen, Heng Huang
In this paper, we propose a faster stochastic alternating direction method of multipliers (ADMM) for nonconvex optimization by using a new stochastic path-integrated differential e…
Momentum-Based Policy Gradient Methods
Feihu Huang, Shangqian Gao, Jian Pei +1
In the paper, we propose a class of efficient momentum-based policy gradient methods for the model-free reinforcement learning, which use adaptive learning rates and do not require…
Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization
Feihu Huang, Shangqian Gao, Jian Pei +1
In the paper, we propose a class of accelerated zeroth-order and first-order momentum methods for both nonconvex mini-optimization and minimax-optimization. Specifically, we propos…
Step-Ahead Error Feedback for Distributed Training with Compressed Gradient
An Xu, Zhouyuan Huo, Heng Huang
Although the distributed machine learning methods can speed up the training of large deep neural networks, the communication cost has become the non-negligible bottleneck to constr…
Faster Secure Data Mining via Distributed Homomorphic Encryption
Junyi Li, Heng Huang
Due to the rising privacy demand in data mining, Homomorphic Encryption (HE) is receiving more and more attention recently for its capability to do computations over the encrypted…
Fast OSCAR and OWL Regression via Safe Screening Rules
Runxue Bao, Bin Gu, Heng Huang
Ordered Weighted (OWL) regularized regression is a new regression analysis for high-dimensional sparse learning. Proximal gradient methods are used as standard approaches t…
Faster Stochastic Quasi-Newton Methods
Qingsong Zhang, Feihu Huang, Cheng Deng +1
Stochastic optimization methods have become a class of popular optimization tools in machine learning. Especially, stochastic gradient descent (SGD) has been widely used for machin…
Detached Error Feedback for Distributed SGD with Random Sparsification
An Xu, Heng Huang
The communication bottleneck has been a critical problem in large-scale distributed deep learning. In this work, we study distributed SGD with random block-wise sparsification as t…
Optimal Gradient Quantization Condition for Communication-Efficient Distributed Training
An Xu, Zhouyuan Huo, Heng Huang
The communication of gradients is costly for training deep neural networks with multiple devices in computer vision applications. In particular, the growing size of deep learning m…
Faster On-Device Training Using New Federated Momentum Algorithm
Zhouyuan Huo, Qian Yang, Bin Gu +1
Mobile crowdsensing has gained significant attention in recent years and has become a critical paradigm for emerging Internet of Things applications. The sensing devices continuous…
Large Batch Training Does Not Need Warmup
Zhouyuan Huo, Bin Gu, Heng Huang
Training deep neural networks using a large batch size has shown promising results and benefits many real-world applications. However, the optimizer converges slowly at early epoch…