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08/21/2023
Label Selection Approach to Learning from Crowds
Supervised learning, especially supervised deep learning, requires large...
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07/26/2023
Regularizing Neural Networks with Meta-Learning Generative Models
This paper investigates methods for improving generative data augmentati...
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07/20/2023
Mitigating Voter Attribute Bias for Fair Opinion Aggregation
The aggregation of multiple opinions plays a crucial role in decision-ma...
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02/25/2023
Mitigating Observation Biases in Crowdsourced Label Aggregation
Crowdsourcing has been widely used to efficiently obtain labeled dataset...
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02/08/2023
Multiview Representation Learning from Crowdsourced Triplet Comparisons
Crowdsourcing has been used to collect data at scale in numerous fields....
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12/20/2022
Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems
Factorization machines (FMs) are a powerful tool for regression and clas...
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11/29/2022
Behavior Estimation from Multi-Source Data for Offline Reinforcement Learning
Offline reinforcement learning (RL) have received rising interest due to...
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10/22/2022
Trustworthy Human Computation: A Survey
Human computation is an approach to solving problems that prove difficul...
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08/21/2022
Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling
The research process includes many decisions, e.g., how to entitle and w...
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06/01/2022
Feature Selection for Discovering Distributional Treatment Effect Modifiers
Finding the features relevant to the difference in treatment effects is ...
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04/27/2022
Transfer Learning with Pre-trained Conditional Generative Models
Transfer learning is crucial in training deep neural networks on new tar...
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12/15/2021
Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations
Intelligent Tutoring Systems have become critically important in future ...
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11/08/2021
Batch Reinforcement Learning from Crowds
A shortcoming of batch reinforcement learning is its requirement for rew...
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06/27/2021
Crowdsourcing Evaluation of Saliency-based XAI Methods
Understanding the reasons behind the predictions made by deep neural net...
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06/11/2021
Inter-domain Multi-relational Link Prediction
Multi-relational graph is a ubiquitous and important data structure, all...
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05/30/2021
Re-evaluating Word Mover's Distance
The word mover's distance (WMD) is a fundamental technique for measuring...
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05/24/2021
Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes
Sequences of events including infectious disease outbreaks, social netwo...
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03/01/2021
Computationally Efficient Wasserstein Loss for Structured Labels
The problem of estimating the probability distribution of labels has bee...
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02/08/2021
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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10/19/2020
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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09/29/2020
GraphITE: Estimating Individual Effects of Graph-structured Treatments
Outcome estimation of treatments for target individuals is an important ...
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09/18/2020
Chemical Property Prediction Under Experimental Biases
The ability to predict the chemical properties of compounds is crucial i...
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08/01/2020
CrowDEA: Multi-view Idea Prioritization with Crowds
Given a set of ideas collected from crowds with regard to an open-ended ...
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06/10/2020
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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06/04/2020
Fast Unbalanced Optimal Transport on Tree
This study examines the time complexities of the unbalanced optimal tran...
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05/11/2020
Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation
Individual treatment effect (ITE) represents the expected improvement in...
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02/08/2020
Random Features Strengthen Graph Neural Networks
Graph neural networks (GNNs) are powerful machine learning models for va...
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02/05/2020
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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05/24/2019
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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02/26/2019
Topological Bayesian Optimization with Persistence Diagrams
Finding an optimal parameter of a black-box function is important for se...
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02/26/2019
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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02/04/2019
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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01/23/2019
Constant Time Graph Neural Networks
Recent advancements in graph neural networks (GNN) have led to state-of-...
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11/08/2018
Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing
Knowledge tracing is a sequence prediction problem where the goal is to ...
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10/04/2018
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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07/04/2018
BayesGrad: Explaining Predictions of Graph Convolutional Networks
Recent advances in graph convolutional networks have significantly impro...
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09/03/2017
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
Item cold-start is a classical issue in recommender systems that affects...
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06/08/2015
Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem
We study the K-armed dueling bandit problem, a variation of the standard...
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03/15/2012
Parametric Return Density Estimation for Reinforcement Learning
Most conventional Reinforcement Learning (RL) algorithms aim to optimize...
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10/05/2010