
Inductive Relational Matrix Completion
Data sparsity and coldstart issues emerge as two major bottlenecks for ...
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GraphOpt: Learning Optimization Models of Graph Formation
Formation mechanisms are fundamental to the study of complex networks, b...
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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...
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Network Diffusions via Neural MeanField Dynamics
We propose a novel learning framework based on neural meanfield dynamic...
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HessianFree HighResolution Nesterov Acceleration for Sampling
We propose an acceleratedgradientbased MCMC method. It relies on a mod...
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HessianFree HighResolution Nesterov Accelerationfor Sampling
We propose an acceleratedgradientbased MCMC method. It relies on a mod...
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Learning to Incentivize Other Learning Agents
The challenge of developing powerful and general Reinforcement Learning ...
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F2A2: Flexible Fullydecentralized Approximate Actorcritic for Cooperative Multiagent Reinforcement Learning
Traditional centralized multiagent reinforcement learning (MARL) algori...
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Learning Cost Functions for Optimal Transport
Learning the cost function for optimal transport from observed transport...
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Transformer Hawkes Process
Modern data acquisition routinely produce massive amounts of event seque...
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Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Nonuniform Subsampling of Gradients
Common Stochastic Gradient MCMC methods approximate gradients by stochas...
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Beyond Clicks: Modeling MultiRelational Item Graph for SessionBased Target Behavior Prediction
Sessionbased target behavior prediction aims to predict the next item t...
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Differentiable Topk Operator with Optimal Transport
The topk operation, i.e., finding the k largest or smallest elements fr...
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HyperMeta Reinforcement Learning with Sparse Reward
Despite their success, existing meta reinforcement learning methods stil...
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Learning Structured Communication for Multiagent Reinforcement Learning
This work explores the largescale multiagent communication mechanism u...
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Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation
Generative Adversarial Networks (GANs) have achieved great success in un...
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Learning Stochastic Behaviour of Aggregate Data
Learning nonlinear dynamics of aggregate data is a challenging problem s...
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Improving DomainAdapted Sentiment Classification by Deep Adversarial Mutual Learning
Domainadapted sentiment classification refers to training on a labeled ...
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Hierarchical Cooperative MultiAgent Reinforcement Learning with Skill Discovery
Human players in professional team sports achieve high level coordinatio...
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Heterogeneous Graphbased Knowledge Transfer for Generalized Zeroshot Learning
Generalized zeroshot learning (GZSL) tackles the problem of learning to...
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Single Episode Policy Transfer in Reinforcement Learning
Transfer and adaptation to new unknown environmental dynamics is a key c...
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Infinitehorizon OffPolicy Policy Evaluation with Multiple Behavior Policies
We consider offpolicy policy evaluation when the trajectory data are ge...
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Learning Robust Representations with Graph Denoising Policy Network
Graph representation learning, aiming to learn lowdimensional represent...
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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...
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Integrating independent and centralized multiagent reinforcement learning for traffic signal network optimization
Traffic congestion in metropolitan areas is a worldwide problem that ca...
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Meta Learning with Relational Information for Short Sequences
This paper proposes a new metalearning method  named HARMLESS (HAwkes...
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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...
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Visual Anomaly Detection in Event Sequence Data
Anomaly detection is a common analytical task that aims to identify rare...
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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...
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On Scalable and Efficient Computation of Large Scale Optimal Transport
Optimal Transport (OT) naturally arises in many machine learning applica...
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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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CM3: Cooperative Multigoal Multistage Multiagent Reinforcement Learning
We propose CM3, a new deep reinforcement learning method for cooperative...
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LinkNBed: MultiGraph Representation Learning with Entity Linkage
Knowledge graphs have emerged as an important model for studying complex...
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Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Learning nonlinear dynamics from diffusion data is a challenging problem...
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Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation
Dynamic treatment recommendation systems based on largescale electronic...
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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 ...
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Iterative Learning with Openset Noisy Labels
Largescale datasets possessing clean label annotations are crucial for ...
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Representation Learning over Dynamic Graphs
How can we effectively encode evolving information over dynamic graphs i...
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A Fast Proximal Point Method for Wasserstein Distance
Wasserstein distance plays increasingly important roles in machine learn...
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Learning to Recommend via Inverse Optimal Matching
We consider recommendation in the context of optimal matching, i.e., we ...
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Visually Explainable Recommendation
Images account for a significant part of user decisions in many applicat...
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Decoupled Learning for Factorial Marked Temporal Point Processes
This paper introduces the factorial marked temporal point process model ...
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tauFPL: ToleranceConstrained Learning in Linear Time
Learning a classifier with control on the falsepositive rate plays a cr...
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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...
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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...
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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...
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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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THAP: A Matlab Toolkit for Learning with Hawkes Processes
As a powerful tool of asynchronous event sequence analysis, point proces...
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Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks
Event sequence, asynchronously generated with random timestamp, is ubiqu...
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Wasserstein Learning of Deep Generative Point Process Models
Point processes are becoming very popular in modeling asynchronous seque...
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Hongyuan Zha
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Professor of Computing of Georgia Institute of Technology, Consultant at Yahoo, Scientific Advisor at proofpoint