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01/28/2023
Decentralized Entropic Optimal Transport for Privacy-preserving Distributed Distribution Comparison
Privacy-preserving distributed distribution comparison measures the dist...
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12/13/2022
Regularized Optimal Transport Layers for Generalized Global Pooling Operations
Global pooling is one of the most significant operations in many machine...
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09/16/2022
ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery
The last decade has witnessed a prosperous development of computational ...
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07/09/2022
Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction
As an essential operation of legal retrieval, legal case matching plays ...
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05/30/2022
Hilbert Curve Projection Distance for Distribution Comparison
Distribution comparison plays a central role in many machine learning ta...
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05/26/2022
Efficient Approximation of Gromov-Wasserstein Distance using Importance Sparsification
As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) dist...
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04/20/2022
Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction
When applying multi-instance learning (MIL) to make predictions for bags...
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01/23/2022
Revisiting Pooling through the Lens of Optimal Transport
Pooling is one of the most significant operations in many machine learni...
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06/21/2021
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
Many representative graph neural networks, e.g., GPR-GNN and ChebyNet, a...
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05/29/2021
Learning Graphon Autoencoders for Generative Graph Modeling
Graphon is a nonparametric model that generates graphs with arbitrary si...
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02/04/2021
Hawkes Processes on Graphons
We propose a novel framework for modeling multiple multivariate point pr...
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12/10/2020
Learning Graphons via Structured Gromov-Wasserstein Barycenters
We propose a novel and principled method to learn a nonparametric graph ...
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11/30/2020
A Hypergradient Approach to Robust Regression without Correspondence
We consider a regression problem, where the correspondence between input...
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06/04/2020
Hierarchical Optimal Transport for Robust Multi-View Learning
Traditional multi-view learning methods often rely on two assumptions: (...
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02/07/2020
Learning Autoencoders with Relational Regularization
A new algorithmic framework is proposed for learning autoencoders of dat...
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12/17/2019
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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11/20/2019
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
We propose a novel graph-driven generative model, that unifies multiple ...
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11/19/2019
Gromov-Wasserstein Factorization Models for Graph Clustering
We propose a new nonlinear factorization model for graphs that are with ...
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10/21/2019
Collaborative Filtering with A Synthetic Feedback Loop
We propose a novel learning framework for recommendation systems, assist...
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10/20/2019
An Optimal Transport Framework for Zero-Shot Learning
We present an optimal transport (OT) framework for generalized zero-shot...
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10/04/2019
Fused Gromov-Wasserstein Alignment for Hawkes Processes
We propose a novel fused Gromov-Wasserstein alignment method to jointly ...
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06/20/2019
Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes
We propose a novel adversarial learning strategy for mixture models of H...
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06/13/2019
Interpretable ICD Code Embeddings with Self- and Mutual-Attention Mechanisms
We propose a novel and interpretable embedding method to represent the i...
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05/18/2019
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
We propose a scalable Gromov-Wasserstein learning (S-GWL) method and est...
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03/17/2019
Topic-Guided Variational Autoencoders for Text Generation
We propose a topic-guided variational autoencoder (TGVAE) model for text...
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03/02/2019
Quaternion Convolutional Neural Networks
Neural networks in the real domain have been studied for a long time and...
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01/17/2019
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
A novel Gromov-Wasserstein learning framework is proposed to jointly mat...
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10/23/2018
PoPPy: A Point Process Toolbox Based on PyTorch
PoPPy is a Point Process toolbox based on PyTorch, which achieves flexib...
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09/12/2018
Distilled Wasserstein Learning for Word Embedding and Topic Modeling
We propose a novel Wasserstein method with a distillation mechanism, yie...
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09/05/2018
Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model
Health risks from cigarette smoking -- the leading cause of preventable ...
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02/13/2018
Superposition-Assisted Stochastic Optimization for Hawkes Processes
We consider the learning of multi-agent Hawkes processes, a model contai...
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01/31/2018
Visually Explainable Recommendation
Images account for a significant part of user decisions in many applicat...
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10/14/2017
Benefits from Superposed Hawkes Processes
The superposition of temporal point processes has been studied for many ...
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10/03/2017
Learning Registered Point Processes from Idiosyncratic Observations
A parametric point process model is developed, with modeling based on th...
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09/13/2017
Flexible Network Binarization with Layer-wise Priority
How to effectively approximate real-valued parameters with binary codes ...
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08/28/2017
THAP: A Matlab Toolkit for Learning with Hawkes Processes
As a powerful tool of asynchronous event sequence analysis, point proces...
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02/22/2017
Learning Hawkes Processes from Short Doubly-Censored Event Sequences
Many real-world applications require robust algorithms to learn point pr...
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01/31/2017
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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09/10/2016
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories
Trajectory analysis is essential in many applications. In this paper, we...
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03/19/2016
Fractal Dimension Invariant Filtering and Its CNN-based Implementation
Fractal analysis has been widely used in computer vision, especially in ...
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02/14/2016
