
Improving Graph Representation Learning by Contrastive Regularization
Graph representation learning is an important task with applications in ...
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The item selection problem for user coldstart recommendation
When a new user just signs up on a website, we usually have no informati...
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Rethinking Graph Regularization For Graph Neural Networks
The graph Laplacian regularization term is usually used in semisupervis...
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Hierarchical Graph Matching Network for Graph Similarity Computation
Graph edit distance / similarity is widely used in many tasks, such as g...
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Understanding Graph Neural Networks from Graph Signal Denoising Perspectives
Graph neural networks (GNNs) have attracted much attention because of th...
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Boosting Firstorder Methods by Shifting Objective: New Schemes with Faster Worst Case Rates
We propose a new methodology to design firstorder methods for unconstra...
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TensorOpt: Exploring the Tradeoffs in Distributed DNN Training with AutoParallelism
A good parallelization strategy can significantly improve the efficiency...
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SelfEnhanced GNN: Improving Graph Neural Networks Using Model Outputs
Graph neural networks (GNNs) have received much attention recently becau...
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Convolutional Embedding for Edit Distance
Editdistancebased string similarity search has many applications such ...
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Edit Distance Embedding using Convolutional Neural Networks
Editdistancebased string similarity search has many applications such ...
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HyperSphere Quantization: CommunicationEfficient SGD for Federated Learning
The high cost of communicating gradients is a major bottleneck for feder...
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NormExplicit Quantization: Improving Vector Quantization for Maximum Inner Product Search
Vector quantization (VQ) techniques are widely used in similarity search...
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Understanding and Improving Proximity Graph based Maximum Inner Product Search
The innerproduct navigable small world graph (ipNSW) represents the st...
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Elastic deep learning in multitenant GPU cluster
Multitenant GPU clusters are common nowadays due to the huge success of...
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Pyramid: A General Framework for Distributed Similarity Search
Similarity search is a core component in various applications such as im...
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NormRange Partition: A Univiseral Catalyst for LSH based Maximum Inner Product Search (MIPS)
Recently, locality sensitive hashing (LSH) was shown to be effective for...
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Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications
The heavytailed distributions of corrupted outliers and singular values...
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ASVRG: Accelerated Proximal SVRG
This paper proposes an accelerated proximal stochastic variance reduced ...
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NormRanging LSH for Maximum Inner Product Search
Neyshabur and Srebro proposed SimpleLSH, which is the stateoftheart ...
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A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
Recent years have witnessed exciting progress in the study of stochastic...
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Tractable and Scalable Schatten QuasiNorm Approximations for Rank Minimization
The Schatten quasinorm was introduced to bridge the gap between the tra...
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VRSGD: A Simple Stochastic Variance Reduction Method for Machine Learning
In this paper, we propose a simple variant of the original SVRG, called ...
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Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization
In this paper, we propose a novel sufficient decrease technique for stoc...
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Scalable De Novo Genome Assembly Using Pregel
De novo genome assembly is the process of stitching short DNA sequences ...
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Accelerated Variance Reduced Stochastic ADMM
Recently, many variance reduced stochastic alternating direction method ...
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Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning
Recently, research on accelerated stochastic gradient descent methods (e...
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Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent
In this paper, we propose a novel sufficient decrease technique for vari...
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Scalable Algorithms for Tractable Schatten QuasiNorm Minimization
The Schattenp quasinorm (0<p<1) is usually used to replace the standar...
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Unified Scalable Equivalent Formulations for Schatten QuasiNorms
The Schatten quasinorm can be used to bridge the gap between the nuclea...
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Quegel: A GeneralPurpose QueryCentric Framework for Querying Big Graphs
Pioneered by Google's Pregel, many distributed systems have been develop...
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Regularized Orthogonal Tensor Decompositions for MultiRelational Learning
Multirelational learning has received lots of attention from researcher...
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Effective Techniques for Message Reduction and Load Balancing in Distributed Graph Computation
Massive graphs, such as online social networks and communication network...
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Structured LowRank Matrix Factorization with Missing and Grossly Corrupted Observations
Recovering lowrank and sparse matrices from incomplete or corrupted obs...
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James Cheng
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Assistant Professor of Department of Computer Science and Engineering at The Chinese University of Hong Kong (CUHK)