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Modeling Interference for Individual Treatment Effect Estimation from Networked Observational Data
Estimating individual treatment effect (ITE) from observational data has attracted great interest in recent years, which plays a crucial role in decision-making across many high-impact domains such as economics, medicine, and e-commerce. Most existing ...
Fair and Private Data Preprocessing through Microaggregation
Privacy protection for personal data and fairness in automated decisions are fundamental requirements for responsible Machine Learning. Both may be enforced through data preprocessing and share a common target: data should remain useful for a task, while ...
From Asset Flow to Status, Action, and Intention Discovery: Early Malice Detection in Cryptocurrency
Cryptocurrency has been subject to illicit activities probably more often than traditional financial assets due to the pseudo-anonymous nature of its transacting entities. An ideal detection model is expected to achieve all three critical properties of ...
EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party
Federated learning (FL) is a machine learning setting which allows multiple participants collaboratively to train a model under the orchestration of a server without disclosing their local data. Vertical federated learning (VFL) is a special structure in ...
SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds
“Sim Sala Bim!” —Silvan,
Betweenness centrality is a popular centrality measure with applications in several domains and whose exact computation is impractical for modern-sized networks. We present SILVAN, ...
History-enhanced and Uncertainty-aware Trajectory Recovery via Attentive Neural Network
A considerable amount of mobility data has been accumulated due to the proliferation of location-based services. Nevertheless, compared with mobility data from transportation systems like the GPS module in taxis, this kind of data is commonly sparse in ...
Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting
The problem of air pollution threatens public health. Air quality forecasting can provide the air quality index hours or even days later, which can help the public to prevent air pollution in advance. Previous works focus on citywide air quality ...
Learning Entangled Interactions of Complex Causality via Self-Paced Contrastive Learning
Learning causality from large-scale text corpora is an important task with numerous applications—for example, in finance, biology, medicine, and scientific discovery. Prior studies have focused mainly on simple causality, which only includes one cause-...
Adaptive Adversarial Contrastive Learning for Cross-Domain Recommendation
Graph-based cross-domain recommendations (CDRs) are useful for suggesting appropriate items because of their promising ability to extract features from user–item interactions and transfer knowledge across domains. Thus, the model can effectively alleviate ...
A Survey on Bid Optimization in Real-Time Bidding Display Advertising
Real-Time Bidding (RTB) is one of the most important forms of online advertising, where an auction is hosted in real time to sell the individual ad impression. How to design an automated bidding strategy in response to the dynamic auction environment is ...
Local Overlapping Spatial-aware Community Detection
Local spatial-aware community detection refers to detecting a spatial-aware community for a given node using local information. A spatial-aware community means that nodes in the community are tightly connected in structure, and their locations are close ...
Graph Domain Adaptation: A Generative View
Recent years have witnessed tremendous interest in deep learning on graph-structured data. Due to the high cost of collecting labeled graph-structured data, domain adaptation is important to supervised graph learning tasks with limited samples. However, ...
Causality-Based Fair Multiple Decision by Response Functions
A recent trend of fair machine learning is to build a decision model subjected to causality-based fairness requirements, which concern with the causality between sensitive attributes and decisions. Almost all (if not all) solutions focus on a single fair ...
LSAB: User Behavioral Pattern Modeling in Sequential Recommendation by Learning Self-Attention Bias
Since the weight of a self-attention model is not affected by the sequence interval, it can more accurately and completely describe the user interests, so it is widely used in processing sequential recommendation. However, the mainstream self-attention ...
Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs
Dynamic graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for many real-world applications such as intrusion identification in network ...
Privacy-Preserving Non-Negative Matrix Factorization with Outliers
Non-negative matrix factorization is a popular unsupervised machine learning algorithm for extracting meaningful features from inherently non-negative data. Such data often contain privacy-sensitive user information. Additionally, the dataset can contain ...
Sparse Grid Imputation Using Unpaired Imprecise Auxiliary Data: Theory and Application to PM2.5 Estimation
Sparse grid imputation (SGI) is a challenging problem, as its goal is to infer the values of the entire grid from a limited number of cells with values. Traditionally, the problem is solved using regression methods such as KNN and kriging, whereas in the ...
Parameter-Agnostic Deep Graph Clustering
Deep graph clustering, efficiently dividing nodes into multiple disjoint clusters in an unsupervised manner, has become a crucial tool for analyzing ubiquitous graph data. Existing methods have acquired impressive clustering effects by optimizing the ...
Learning Hierarchical Task Structures for Few-shot Graph Classification
The problem of few-shot graph classification targets at assigning class labels for graph samples, where only limited labeled graphs are provided for each class. To solve the problem brought by label scarcity, recent studies have proposed to adopt the ...
Three-stage Transferable and Generative Crowdsourced Comment Integration Framework Based on Zero- and Few-shot Learning with Domain Distribution Alignment
Online shopping has become a crucial way to encourage daily consumption, where the User-generated, or crowdsourced product comments, can offer a broad range of feedback on e-commerce products. As a result, integrating critical opinions or major attitudes ...
Efficient Version Space Algorithms for Human-in-the-loop Model Development
When active learning (AL) is applied to help users develop a model on a large dataset through interactively presenting data instances for labeling, existing AL techniques often suffer from two main drawbacks: First, to reach high accuracy they may require ...
StructCoder: Structure-Aware Transformer for Code Generation
There has been a recent surge of interest in automating software engineering tasks using deep learning. This article addresses the problem of code generation, in which the goal is to generate target code given source code in a different language or a ...
DEWP: Deep Expansion Learning for Wind Power Forecasting
Wind is one kind of high-efficient, environmentally-friendly, and cost-effective energy source. Wind power, as one of the largest renewable energy in the world, has been playing a more and more important role in supplying electricity. Though growing ...
Enhanced Fuzzy Clustering for Incomplete Instance with Evidence Combination
Clustering incomplete instance is still a challenging task since missing values maybe make the cluster information ambiguous, leading to the uncertainty and imprecision in results. This article investigates an enhanced fuzzy clustering with evidence ...