
Learning Graphons via Structured GromovWasserstein Barycenters
We propose a novel and principled method to learn a nonparametric graph ...
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A Hypergradient Approach to Robust Regression without Correspondence
We consider a regression problem, where the correspondence between input...
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Hierarchical Optimal Transport for Robust MultiView Learning
Traditional multiview learning methods often rely on two assumptions: (...
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Learning Autoencoders with Relational Regularization
A new algorithmic framework is proposed for learning autoencoders of dat...
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Quaternion Product Units for Deep Learning on 3D Rotation Groups
We propose a novel quaternion product unit (QPU) to represent data on 3D...
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GraphDriven Generative Models for Heterogeneous MultiTask Learning
We propose a novel graphdriven generative model, that unifies multiple ...
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GromovWasserstein Factorization Models for Graph Clustering
We propose a new nonlinear factorization model for graphs that are with ...
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Collaborative Filtering with A Synthetic Feedback Loop
We propose a novel learning framework for recommendation systems, assist...
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An Optimal Transport Framework for ZeroShot Learning
We present an optimal transport (OT) framework for generalized zeroshot...
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Fused GromovWasserstein Alignment for Hawkes Processes
We propose a novel fused GromovWasserstein alignment method to jointly ...
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Adversarial SelfPaced Learning for Mixture Models of Hawkes Processes
We propose a novel adversarial learning strategy for mixture models of H...
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Interpretable ICD Code Embeddings with Self and MutualAttention Mechanisms
We propose a novel and interpretable embedding method to represent the i...
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Scalable GromovWasserstein Learning for Graph Partitioning and Matching
We propose a scalable GromovWasserstein learning (SGWL) method and est...
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TopicGuided Variational Autoencoders for Text Generation
We propose a topicguided variational autoencoder (TGVAE) model for text...
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Quaternion Convolutional Neural Networks
Neural networks in the real domain have been studied for a long time and...
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GromovWasserstein Learning for Graph Matching and Node Embedding
A novel GromovWasserstein learning framework is proposed to jointly mat...
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PoPPy: A Point Process Toolbox Based on PyTorch
PoPPy is a Point Process toolbox based on PyTorch, which achieves flexib...
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Distilled Wasserstein Learning for Word Embedding and Topic Modeling
We propose a novel Wasserstein method with a distillation mechanism, yie...
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Predicting Smoking Events with a TimeVarying SemiParametric Hawkes Process Model
Health risks from cigarette smoking  the leading cause of preventable ...
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SuperpositionAssisted Stochastic Optimization for Hawkes Processes
We consider the learning of multiagent Hawkes processes, a model contai...
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Visually Explainable Recommendation
Images account for a significant part of user decisions in many applicat...
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Benefits from Superposed Hawkes Processes
The superposition of temporal point processes has been studied for many ...
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Learning Registered Point Processes from Idiosyncratic Observations
A parametric point process model is developed, with modeling based on th...
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Flexible Network Binarization with Layerwise Priority
How to effectively approximate realvalued parameters with binary codes ...
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THAP: A Matlab Toolkit for Learning with Hawkes Processes
As a powerful tool of asynchronous event sequence analysis, point proces...
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Learning Hawkes Processes from Short DoublyCensored Event Sequences
Many realworld applications require robust algorithms to learn point pr...
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A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering
We propose an effective method to solve the event sequence clustering pr...
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A TubeandDropletbased Approach for Representing and Analyzing Motion Trajectories
Trajectory analysis is essential in many applications. In this paper, we...
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Fractal Dimension Invariant Filtering and Its CNNbased Implementation
Fractal analysis has been widely used in computer vision, especially in ...
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Learning Granger Causality for Hawkes Processes
Learning Granger causality for general point processes is a very challen...
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Hongteng Xu
verfied profile
Postdoctoral Researcher at Duke University