
Interdomain Multirelational Link Prediction
Multirelational graph is a ubiquitous and important data structure, all...
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Reevaluating Word Mover's Distance
The word mover's distance (WMD) is a fundamental technique for measuring...
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Dynamic Hawkes Processes for Discovering Timeevolving Communities' States behind Diffusion Processes
Sequences of events including infectious disease outbreaks, social netwo...
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Computationally Efficient Wasserstein Loss for Structured Labels
The problem of estimating the probability distribution of labels has bee...
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Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference
Crowd movement guidance has been a fascinating problem in various fields...
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Poincare: Recommending Publication Venues via Treatment Effect Estimation
Choosing a publication venue for an academic paper is a crucial step in ...
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GraphITE: Estimating Individual Effects of Graphstructured Treatments
Outcome estimation of treatments for target individuals is an important ...
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Chemical Property Prediction Under Experimental Biases
The ability to predict the chemical properties of compounds is crucial i...
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CrowDEA: Multiview Idea Prioritization with Crowds
Given a set of ideas collected from crowds with regard to an openended ...
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Regret Minimization for Causal Inference on Large Treatment Space
Predicting which action (treatment) will lead to a better outcome is a c...
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Fast Unbalanced Optimal Transport on Tree
This study examines the time complexities of the unbalanced optimal tran...
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Counterfactual Propagation for SemiSupervised Individual Treatment Effect Estimation
Individual treatment effect (ITE) represents the expected improvement in...
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Random Features Strengthen Graph Neural Networks
Graph neural networks (GNNs) are powerful machine learning models for va...
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Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces
The problem of comparing distributions endowed with their own geometry a...
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Approximation Ratios of Graph Neural Networks for Combinatorial Problems
In this paper, from a theoretical perspective, we study how powerful gra...
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Topological Bayesian Optimization with Persistence Diagrams
Finding an optimal parameter of a blackbox function is important for se...
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Learning to Find Hard Instances of Graph Problems
Finding hard instances, which need a long time to solve, of graph proble...
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Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family
This paper investigates the theory of robustness against adversarial att...
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Constant Time Graph Neural Networks
Recent advancements in graph neural networks (GNN) have led to stateof...
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Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing
Knowledge tracing is a sequence prediction problem where the goal is to ...
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Dual Convolutional Neural Network for Graph of Graphs Link Prediction
Graphs are general and powerful data representations which can model com...
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BayesGrad: Explaining Predictions of Graph Convolutional Networks
Recent advances in graph convolutional networks have significantly impro...
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Using Posters to Recommend Anime and Mangas in a ColdStart Scenario
Item coldstart is a classical issue in recommender systems that affects...
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Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem
We study the Karmed dueling bandit problem, a variation of the standard...
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Parametric Return Density Estimation for Reinforcement Learning
Most conventional Reinforcement Learning (RL) algorithms aim to optimize...
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Estimation of lowrank tensors via convex optimization
In this paper, we propose three approaches for the estimation of the Tuc...
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Hisashi Kashima
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