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Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems
Quanming Yao, James T. Kwok, Fei Gao +2
The proximal gradient algorithm has been popularly used for convex optimization. Recently, it has also been extended for nonconvex problems, and the current state-of-the-art is the…
Randomized Mechanisms for Selling Reserved Instances in Cloud
Jia Zhang, Weidong Ma, Tao Qin +2
Selling reserved instances (or virtual machines) is a basic service in cloud computing. In this paper, we consider a more flexible pricing model for instance reservation, in which…
A Communication-Efficient Parallel Algorithm for Decision Tree
Qi Meng, Guolin Ke, Taifeng Wang +4
Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Forest) is a widely used machine learning algorithm, due to its practical effectiveness and mo…
Dual Learning for Machine Translation
Yingce Xia, Di He, Tao Qin +4
While neural machine translation (NMT) is making good progress in the past two years, tens of millions of bilingual sentence pairs are needed for its training. However, human label…
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks
Xiang Li, Tao Qin, Jian Yang +1
Recurrent neural networks (RNNs) have achieved state-of-the-art performances in many natural language processing tasks, such as language modeling and machine translation. However,…
Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction
Qi Meng, Wei Chen, Jingcheng Yu +3
Regularized empirical risk minimization (R-ERM) is an important branch of machine learning, since it constrains the capacity of the hypothesis space and guarantees the generalizati…
Generalization Error Bounds for Optimization Algorithms via Stability
Qi Meng, Yue Wang, Wei Chen +3
Many machine learning tasks can be formulated as Regularized Empirical Risk Minimization (R-ERM), and solved by optimization algorithms such as gradient descent (GD), stochastic gr…
Asynchronous Stochastic Gradient Descent with Delay Compensation
Shuxin Zheng, Qi Meng, Taifeng Wang +4
With the fast development of deep learning, it has become common to learn big neural networks using massive training data. Asynchronous Stochastic Gradient Descent (ASGD) is widely…
Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks
Shizhao Sun, Wei Chen, Jiang Bian +2
Parallelization framework has become a necessity to speed up the training of deep neural networks (DNN) recently. Such framework typically employs the Model Average approach, denot…
Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves
Fei Tian, Bin Gao, Di He +1
We propose Sentence Level Recurrent Topic Model (SLRTM), a new topic model that assumes the generation of each word within a sentence to depend on both the topic of the sentence an…