Hongyuan Zha

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Georgia Institute of Technology ∙ East China Normal University ∙ The Chinese University of Hong Kong, Shenzhen ∙ USTC

Professor of Computing of Georgia Institute of Technology, Consultant at Yahoo, Scientific Advisor at proofpoint

Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation

Generative Adversarial Networks (GANs) have achieved great success in un...
02/10/2020 ∙ by Minshuo Chen, et al. ∙ 45 ∙ share

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Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery

Human players in professional team sports achieve high level coordinatio...
12/07/2019 ∙ by Jiachen Yang, et al. ∙ 31 ∙ share

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Meta Learning with Relational Information for Short Sequences

This paper proposes a new meta-learning method -- named HARMLESS (HAwkes...
09/04/2019 ∙ by Yujia Xie, et al. ∙ 22 ∙ share

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Learning Robust Representations with Graph Denoising Policy Network

Graph representation learning, aiming to learn low-dimensional represent...
10/04/2019 ∙ by Lu Wang, et al. ∙ 21 ∙ share

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Learning Structured Communication for Multi-agent Reinforcement Learning

This work explores the large-scale multi-agent communication mechanism u...
02/11/2020 ∙ by Junjie Sheng, et al. ∙ 16 ∙ share

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On Scalable and Efficient Computation of Large Scale Optimal Transport

Optimal Transport (OT) naturally arises in many machine learning applica...
05/01/2019 ∙ by Yujia Xie, et al. ∙ 14 ∙ share

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Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies

We consider off-policy policy evaluation when the trajectory data are ge...
10/10/2019 ∙ by Xinyun Chen, et al. ∙ 13 ∙ share

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Learning Stochastic Behaviour of Aggregate Data

Learning nonlinear dynamics of aggregate data is a challenging problem s...
02/10/2020 ∙ by Shaojun Ma, et al. ∙ 13 ∙ share

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Gromov-Wasserstein Learning for Graph Matching and Node Embedding

A novel Gromov-Wasserstein learning framework is proposed to jointly mat...
01/17/2019 ∙ by Hongteng Xu, et al. ∙ 12 ∙ share

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Single Episode Policy Transfer in Reinforcement Learning

Transfer and adaptation to new unknown environmental dynamics is a key c...
10/17/2019 ∙ by Jiachen Yang, et al. ∙ 12 ∙ share

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Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning

Generalized zero-shot learning (GZSL) tackles the problem of learning to...
11/20/2019 ∙ by Junjie Wang, et al. ∙ 12 ∙ share

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Stein Bridging: Enabling Mutual Reinforcement between Explicit and Implicit Generative Models

Deep generative models are generally categorized into explicit models an...
09/28/2019 ∙ by Qitian Wu, et al. ∙ 11 ∙ share

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Learning Cost Functions for Optimal Transport

Learning the cost function for optimal transport from observed transport...
02/22/2020 ∙ by Haodong Sun, et al. ∙ 10 ∙ share

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Hyper-Meta Reinforcement Learning with Sparse Reward

Despite their success, existing meta reinforcement learning methods stil...
02/11/2020 ∙ by Yun Hua, et al. ∙ 9 ∙ share

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F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

Traditional centralized multi-agent reinforcement learning (MARL) algori...
04/17/2020 ∙ by Wenhao Li, et al. ∙ 9 ∙ share

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Differentiable Top-k Operator with Optimal Transport

The top-k operation, i.e., finding the k largest or smallest elements fr...
02/16/2020 ∙ by Yujia Xie, et al. ∙ 6 ∙ share

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Transformer Hawkes Process

Modern data acquisition routinely produce massive amounts of event seque...
02/21/2020 ∙ by Simiao Zuo, et al. ∙ 5 ∙ share

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Reinforcement Learning with Policy Mixture Model for Temporal Point Processes Clustering

Temporal point process is an expressive tool for modeling event sequence...
05/29/2019 ∙ by Weichang Wu, et al. ∙ 4 ∙ share

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Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients

Common Stochastic Gradient MCMC methods approximate gradients by stochas...
02/20/2020 ∙ by Ruilin Li, et al. ∙ 3 ∙ share

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Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation

Dynamic treatment recommendation systems based on large-scale electronic...
07/04/2018 ∙ by Lu Wang, et al. ∙ 2 ∙ share

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Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization

Traffic congestion in metropolitan areas is a world-wide problem that ca...
09/23/2019 ∙ by Zhi Zhang, et al. ∙ 2 ∙ share

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Learning Registered Point Processes from Idiosyncratic Observations

A parametric point process model is developed, with modeling based on th...
10/03/2017 ∙ by Hongteng Xu, et al. ∙ 0 ∙ share

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THAP: A Matlab Toolkit for Learning with Hawkes Processes

As a powerful tool of asynchronous event sequence analysis, point proces...
08/28/2017 ∙ by Hongteng Xu, et al. ∙ 0 ∙ share

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Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks

Event sequence, asynchronously generated with random timestamp, is ubiqu...
05/24/2017 ∙ by Shuai Xiao, et al. ∙ 0 ∙ share

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Wasserstein Learning of Deep Generative Point Process Models

Point processes are becoming very popular in modeling asynchronous seque...
05/23/2017 ∙ by Shuai Xiao, et al. ∙ 0 ∙ share

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Recurrent Poisson Factorization for Temporal Recommendation

Poisson factorization is a probabilistic model of users and items for re...
03/04/2017 ∙ by Seyed Abbas Hosseini, et al. ∙ 0 ∙ share

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Learning Hawkes Processes from Short Doubly-Censored Event Sequences

Many real-world applications require robust algorithms to learn point pr...
02/22/2017 ∙ by Hongteng Xu, et al. ∙ 0 ∙ share

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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...
01/31/2017 ∙ by Hongteng Xu, et al. ∙ 0 ∙ share

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Learning Granger Causality for Hawkes Processes

Learning Granger causality for general point processes is a very challen...
02/14/2016 ∙ by Hongteng Xu, et al. ∙ 0 ∙ share

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COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution

Information diffusion in online social networks is affected by the under...
07/08/2015 ∙ by Mehrdad Farajtabar, et al. ∙ 0 ∙ share

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A constrained clustering based approach for matching a collection of feature sets

In this paper, we consider the problem of finding the feature correspond...
06/12/2016 ∙ by Junchi Yan, et al. ∙ 0 ∙ share

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Fractal Dimension Invariant Filtering and Its CNN-based Implementation

Fractal analysis has been widely used in computer vision, especially in ...
03/19/2016 ∙ by Hongteng Xu, et al. ∙ 0 ∙ share

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A General Multi-Graph Matching Approach via Graduated Consistency-regularized Boosting

This paper addresses the problem of matching N weighted graphs referring...
02/20/2015 ∙ by Junchi Yan, et al. ∙ 0 ∙ share

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Hybrid Generative/Discriminative Learning for Automatic Image Annotation

Automatic image annotation (AIA) raises tremendous challenges to machine...
03/15/2012 ∙ by Shuang Hong Yang, et al. ∙ 0 ∙ share

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Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment

Nonlinear manifold learning from unorganized data points is a very chall...
12/07/2002 ∙ by Zhenyue Zhang, et al. ∙ 0 ∙ share

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tau-FPL: Tolerance-Constrained Learning in Linear Time

Learning a classifier with control on the false-positive rate plays a cr...
01/15/2018 ∙ by Ao Zhang, et al. ∙ 0 ∙ share

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Decoupled Learning for Factorial Marked Temporal Point Processes

This paper introduces the factorial marked temporal point process model ...
01/21/2018 ∙ by Weichang Wu, et al. ∙ 0 ∙ share

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Learning to Recommend via Inverse Optimal Matching

We consider recommendation in the context of optimal matching, i.e., we ...
02/10/2018 ∙ by Ruilin Li, et al. ∙ 0 ∙ share

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A Fast Proximal Point Method for Wasserstein Distance

Wasserstein distance plays increasingly important roles in machine learn...
02/12/2018 ∙ by Yujia Xie, et al. ∙ 0 ∙ share

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Representation Learning over Dynamic Graphs

How can we effectively encode evolving information over dynamic graphs i...
03/11/2018 ∙ by Rakshit Trivedi, et al. ∙ 0 ∙ share

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Iterative Learning with Open-set Noisy Labels

Large-scale datasets possessing clean label annotations are crucial for ...
03/31/2018 ∙ by Yisen Wang, et al. ∙ 0 ∙ share

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Deep Mean Field Games for Learning Optimal Behavior Policy of Large Populations

We consider the problem of representing a large population's behavior po...
11/08/2017 ∙ by Jiachen Yang, et al. ∙ 0 ∙ share

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Learning to Optimize via Wasserstein Deep Inverse Optimal Control

We study the inverse optimal control problem in social sciences: we aim ...
05/22/2018 ∙ by Yichen Wang, et al. ∙ 0 ∙ share

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Learning Deep Hidden Nonlinear Dynamics from Aggregate Data

Learning nonlinear dynamics from diffusion data is a challenging problem...
07/22/2018 ∙ by Yisen Wang, et al. ∙ 0 ∙ share

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LinkNBed: Multi-Graph Representation Learning with Entity Linkage

Knowledge graphs have emerged as an important model for studying complex...
07/23/2018 ∙ by Rakshit Trivedi, et al. ∙ 0 ∙ share

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Learning Deep Mean Field Games for Modeling Large Population Behavior

We consider the problem of representing collective behavior of large pop...
11/08/2017 ∙ by Jiachen Yang, et al. ∙ 0 ∙ share

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CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning

We propose CM3, a new deep reinforcement learning method for cooperative...
09/13/2018 ∙ by Jiachen Yang, et al. ∙ 0 ∙ share

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Hawkes Processes for Invasive Species Modeling and Management

The spread of invasive species to new areas threatens the stability of e...
12/12/2017 ∙ by Amrita Gupta, et al. ∙ 0 ∙ share

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Visually Explainable Recommendation

Images account for a significant part of user decisions in many applicat...
01/31/2018 ∙ by Xu Chen, et al. ∙ 0 ∙ share

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Modeling Event Propagation via Graph Biased Temporal Point Process

Temporal point process is widely used for sequential data modeling. In t...
08/05/2019 ∙ by Weichang Wu, et al. ∙ 0 ∙ share

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Hongyuan Zha

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