
A Survey of Labelnoise Representation Learning: Past, Present and Future
Classical machine learning implicitly assumes that labels of the trainin...
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Efficient LowRank Matrix Learning by Factorizable Nonconvex Regularization
Matrix learning is at the core of many machine learning problems. To enc...
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Effective Decoding in Graph AutoEncoder using Triadic Closure
The (variational) graph autoencoder and its variants have been popularl...
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CommunicationEfficient Distributed Blockwise Momentum SGD with ErrorFeedback
Communication overhead is a major bottleneck hampering the scalability o...
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Blockwise Adaptivity: Faster Training and Better Generalization in Deep Learning
Stochastic methods with coordinatewise adaptive stepsize (such as RMSpr...
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General Convolutional Sparse Coding with Unknown Noise
Convolutional sparse coding (CSC) can learn representative shiftinvaria...
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Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Variance reduction has been commonly used in stochastic optimization. It...
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Power Law in Sparsified Deep Neural Networks
The power law has been observed in the degree distributions of many biol...
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Online Convolutional Sparse Coding with SampleDependent Dictionary
Convolutional sparse coding (CSC) has been popularly used for the learni...
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Lossaware Weight Quantization of Deep Networks
The huge size of deep networks hinders their use in small computing devi...
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Scalable Robust Matrix Factorization with Nonconvex Loss
Robust matrix factorization (RMF), which uses the ℓ_1loss, often outper...
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Efficient Robust Matrix Factorization with Nonconvex Loss
Robust matrix factorization (RMF), which uses the ℓ_1loss, often outper...
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LargeScale LowRank Matrix Learning with Nonconvex Regularizers
Lowrank modeling has many important applications in computer vision and...
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Scalable Online Convolutional Sparse Coding
Convolutional sparse coding (CSC) improves sparse coding by learning a s...
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MultiLabel Learning with Global and Local Label Correlation
It is wellknown that exploiting label correlations is important to mult...
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Accelerated and Inexact SoftImpute for LargeScale Matrix and Tensor Completion
Matrix and tensor completion aim to recover a lowrank matrix / tensor f...
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Lossaware Binarization of Deep Networks
Deep neural network models, though very powerful and highly successful, ...
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Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
The use of convex regularizers allows for easy optimization, though they...
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Stochastic VarianceReduced ADMM
The alternating direction method of multipliers (ADMM) is a powerful opt...
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Fast Nonsmooth Regularized Risk Minimization with Continuation
In regularized risk minimization, the associated optimization problem be...
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Fast LowRank Matrix Learning with Nonconvex Regularization
Lowrank modeling has a lot of important applications in machine learnin...
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James T. Kwok
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Professor in the Department of Computer Science and Engineering at Hong Kong University of Science and Technology