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04/22/2023
Towards Understanding Feature Learning in Out-of-Distribution Generalization
A common explanation for the failure of out-of-distribution (OOD) genera...
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11/28/2022
DGI: Easy and Efficient Inference for GNNs
While many systems have been developed to train Graph Neural Networks (G...
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06/27/2022
A Representation Learning Framework for Property Graphs
Representation learning on graphs, also called graph embedding, has demo...
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06/27/2022
Measuring and Improving the Use of Graph Information in Graph Neural Networks
Graph neural networks (GNNs) have been widely used for representation le...
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06/27/2022
Efficient Private SCO for Heavy-Tailed Data via Clipping
We consider stochastic convex optimization for heavy-tailed data with th...
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06/15/2022
Pareto Invariant Risk Minimization
Despite the success of invariant risk minimization (IRM) in tackling the...
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06/15/2022
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack
The AutoAttack (AA) has been the most reliable method to evaluate advers...
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02/16/2022
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
Recently Graph Injection Attack (GIA) emerges as a practical attack scen...
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02/11/2022
Invariance Principle Meets Out-of-Distribution Generalization on Graphs
Despite recent developments in using the invariance principle from causa...
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09/30/2021
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization
We show that stochastic acceleration can be achieved under the perturbed...
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06/30/2021
Local Reweighting for Adversarial Training
Instances-reweighted adversarial training (IRAT) can significantly boost...
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05/25/2021
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums
The problem of finding near-stationary points in convex optimization has...
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05/10/2021
G-Tran: Making Distributed Graph Transactions Fast
Graph transaction processing raises many unique challenges such as rando...
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01/27/2021
Improving Graph Representation Learning by Contrastive Regularization
Graph representation learning is an important task with applications in ...
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10/27/2020
The item selection problem for user cold-start recommendation
When a new user just signs up on a website, we usually have no informati...
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09/04/2020
Rethinking Graph Regularization For Graph Neural Networks
The graph Laplacian regularization term is usually used in semi-supervis...
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06/30/2020
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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06/08/2020
Understanding Graph Neural Networks from Graph Signal Denoising Perspectives
Graph neural networks (GNNs) have attracted much attention because of th...
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05/25/2020
Boosting First-order Methods by Shifting Objective: New Schemes with Faster Worst Case Rates
We propose a new methodology to design first-order methods for unconstra...
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04/16/2020
TensorOpt: Exploring the Tradeoffs in Distributed DNN Training with Auto-Parallelism
A good parallelization strategy can significantly improve the efficiency...
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02/18/2020
Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs
Graph neural networks (GNNs) have received much attention recently becau...
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01/31/2020
Convolutional Embedding for Edit Distance
Edit-distance-based string similarity search has many applications such ...
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01/31/2020
Edit Distance Embedding using Convolutional Neural Networks
Edit-distance-based string similarity search has many applications such ...
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11/12/2019
Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning
The high cost of communicating gradients is a major bottleneck for feder...
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11/12/2019
Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search
Vector quantization (VQ) techniques are widely used in similarity search...
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09/30/2019
Understanding and Improving Proximity Graph based Maximum Inner Product Search
The inner-product navigable small world graph (ip-NSW) represents the st...
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09/26/2019
Elastic deep learning in multi-tenant GPU cluster
Multi-tenant GPU clusters are common nowadays due to the huge success of...
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06/25/2019
Pyramid: A General Framework for Distributed Similarity Search
Similarity search is a core component in various applications such as im...
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10/22/2018
Norm-Range 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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10/11/2018
Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications
The heavy-tailed distributions of corrupted outliers and singular values...
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10/07/2018
ASVRG: Accelerated Proximal SVRG
This paper proposes an accelerated proximal stochastic variance reduced ...
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09/24/2018
Norm-Ranging LSH for Maximum Inner Product Search
Neyshabur and Srebro proposed Simple-LSH, which is the state-of-the-art ...
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06/28/2018
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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02/28/2018
Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization
The Schatten quasi-norm was introduced to bridge the gap between the tra...
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02/26/2018
VR-SGD: 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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02/26/2018
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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01/13/2018
Scalable De Novo Genome Assembly Using Pregel
De novo genome assembly is the process of stitching short DNA sequences ...
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07/11/2017
Accelerated Variance Reduced Stochastic ADMM
Recently, many variance reduced stochastic alternating direction method ...
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03/23/2017
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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03/20/2017
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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06/04/2016
Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization
The Schatten-p quasi-norm (0<p<1) is usually used to replace the standar...
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06/02/2016
Unified Scalable Equivalent Formulations for Schatten Quasi-Norms
The Schatten quasi-norm can be used to bridge the gap between the nuclea...
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01/25/2016
Quegel: A General-Purpose Query-Centric Framework for Querying Big Graphs
Pioneered by Google's Pregel, many distributed systems have been develop...
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12/26/2015
Regularized Orthogonal Tensor Decompositions for Multi-Relational Learning
Multi-relational learning has received lots of attention from researcher...
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03/02/2015
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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09/03/2014