Hongyuan Zha

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Temple University ∙ 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

Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances

For the traveling salesman problem (TSP), the existing supervised learni...
12/19/2020 ∙ by Zhang-Hua Fu, et al. ∙ 0 ∙ share

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Learning Graphons via Structured Gromov-Wasserstein Barycenters

We propose a novel and principled method to learn a nonparametric graph ...
12/10/2020 ∙ by Hongteng Xu, et al. ∙ 0 ∙ share

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A Hypergradient Approach to Robust Regression without Correspondence

We consider a regression problem, where the correspondence between input...
11/30/2020 ∙ by Yujia Xie, et al. ∙ 0 ∙ share

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Reliable Off-policy Evaluation for Reinforcement Learning

In a sequential decision-making problem, off-policy evaluation (OPE) est...
11/08/2020 ∙ by Jie Wang, et al. ∙ 0 ∙ share

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Inductive Relational Matrix Completion

Data sparsity and cold-start issues emerge as two major bottlenecks for ...
07/09/2020 ∙ by Qitian Wu, et al. ∙ 0 ∙ share

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GraphOpt: Learning Optimization Models of Graph Formation

Formation mechanisms are fundamental to the study of complex networks, b...
07/07/2020 ∙ by Rakshit Trivedi, et al. ∙ 18 ∙ share

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Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification

Graph neural networks are promising architecture for learning and infere...
06/29/2020 ∙ by Kai Zhang, et al. ∙ 2 ∙ share

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Network Diffusions via Neural Mean-Field Dynamics

We propose a novel learning framework based on neural mean-field dynamic...
06/16/2020 ∙ by Shushan He, et al. ∙ 0 ∙ share

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Hessian-Free High-Resolution Nesterov Acceleration for Sampling

We propose an accelerated-gradient-based MCMC method. It relies on a mod...
06/16/2020 ∙ by Ruilin Li, et al. ∙ 0 ∙ share

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Hessian-Free High-Resolution Nesterov Accelerationfor Sampling

We propose an accelerated-gradient-based MCMC method. It relies on a mod...
06/16/2020 ∙ by Ruilin Li, et al. ∙ 0 ∙ share

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Learning to Incentivize Other Learning Agents

The challenge of developing powerful and general Reinforcement Learning ...
06/10/2020 ∙ by Jiachen Yang, et al. ∙ 0 ∙ 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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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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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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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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Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

Session-based target behavior prediction aims to predict the next item t...
02/19/2020 ∙ by Wen Wang, et al. ∙ 0 ∙ 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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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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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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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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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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Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning

Domain-adapted sentiment classification refers to training on a labeled ...
02/01/2020 ∙ by Qianming Xue, et al. ∙ 0 ∙ 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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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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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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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 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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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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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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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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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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Visual Anomaly Detection in Event Sequence Data

Anomaly detection is a common analytical task that aims to identify rare...
06/26/2019 ∙ by Shunan Guo, et al. ∙ 0 ∙ 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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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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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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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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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 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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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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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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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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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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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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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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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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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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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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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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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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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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Hongyuan Zha

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