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09/15/2023
Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions
How to properly set the privacy parameter in differential privacy (DP) h...
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09/15/2023
Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach
Graph Neural Networks have achieved tremendous success in modeling compl...
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09/15/2023
HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks
While numerous defense methods have been proposed to prohibit potential ...
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09/14/2023
On Prediction Feature Assignment in the Heckman Selection Model
Under missing-not-at-random (MNAR) sample selection bias, the performanc...
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08/19/2023
Robust Fraud Detection via Supervised Contrastive Learning
Deep learning models have recently become popular for detecting maliciou...
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06/02/2023
Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
Learning disentangled causal representations is a challenging problem th...
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05/31/2023
Towards Fair Disentangled Online Learning for Changing Environments
In the problem of online learning for changing environments, data are se...
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05/25/2023
A Robust Classifier Under Missing-Not-At-Random Sample Selection Bias
The shift between the training and testing distributions is commonly due...
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11/10/2022
Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks
Graph neural networks (GNNs) are susceptible to privacy inference attack...
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10/09/2022
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations
Anomaly detection in sequential data has been studied for a long time be...
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05/20/2022
Adaptive Fairness-Aware Online Meta-Learning for Changing Environments
The fairness-aware online learning framework has arisen as a powerful to...
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02/15/2022
Trustworthy Anomaly Detection: A Survey
Anomaly detection has a wide range of real-world applications, such as b...
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01/18/2022
Model Transferring Attacks to Backdoor HyperNetwork in Personalized Federated Learning
This paper explores previously unknown backdoor risks in HyperNet-based ...
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01/13/2022
The Fairness Field Guide: Perspectives from Social and Formal Sciences
Over the past several years, a slew of different methods to measure the ...
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10/17/2021
Poisoning Attacks on Fair Machine Learning
Both fair machine learning and adversarial learning have been extensivel...
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10/08/2021
Fair Regression under Sample Selection Bias
Recent research on fair regression focused on developing new fairness no...
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09/21/2021
Achieving Counterfactual Fairness for Causal Bandit
In online recommendation, customers arrive in a sequential and stochasti...
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05/24/2021
Robust Fairness-aware Learning Under Sample Selection Bias
The underlying assumption of many machine learning algorithms is that th...
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05/24/2021
Classifying Math KCs via Task-Adaptive Pre-Trained BERT
Educational content labeled with proper knowledge components (KCs) are p...
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05/24/2021
MultiFair: Multi-Group Fairness in Machine Learning
Algorithmic fairness is becoming increasingly important in data mining a...
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03/07/2021
LogBERT: Log Anomaly Detection via BERT
Detecting anomalous events in online computer systems is crucial to prot...
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10/22/2020
Achieving User-Side Fairness in Contextual Bandits
Personalized recommendation based on multi-arm bandit (MAB) algorithms h...
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10/10/2020
Fairness-aware Agnostic Federated Learning
Federated learning is an emerging framework that builds centralized mach...
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05/25/2020
Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities
Insider threats, as one type of the most challenging threats in cyberspa...
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03/08/2020
Removing Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy
When we enforce differential privacy in machine learning, the utility-pr...
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11/12/2019
Identifying Hidden Buyers in Darknet Markets via Dirichlet Hawkes Process
The darknet markets are notorious black markets in cyberspace, which inv...
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11/11/2019
Achieving Differential Privacy in Vertically Partitioned Multiparty Learning
Preserving differential privacy has been well studied under centralized ...
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11/11/2019
Fairness through Equality of Effort
Fair machine learning is receiving an increasing attention in machine le...
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10/20/2019
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
A recent trend of fair machine learning is to define fairness as causali...
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10/08/2019
Insider Threat Detection via Hierarchical Neural Temporal Point Processes
Insiders usually cause significant losses to organizations and are hard ...
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06/02/2019
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness
In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM)...
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09/13/2018
Fairness-aware Classification: Criterion, Convexity, and Bounds
Fairness-aware classification is receiving increasing attention in the m...
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09/12/2018
SAFE: A Neural Survival Analysis Model for Fraud Early Detection
Many online platforms have deployed anti-fraud systems to detect and pre...
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05/28/2018
FairGAN: Fairness-aware Generative Adversarial Networks
Fairness-aware learning is increasingly important in data mining. Discri...
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03/05/2018
On Discrimination Discovery and Removal in Ranked Data using Causal Graph
Predictive models learned from historical data are widely used to help c...
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03/05/2018
One-Class Adversarial Nets for Fraud Detection
Many online applications, such as online social networks or knowledge ba...
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09/18/2017
Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning
In this paper, we focus on developing a novel mechanism to preserve diff...
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06/25/2017
Preserving Differential Privacy in Convolutional Deep Belief Networks
The remarkable development of deep learning in medicine and healthcare d...
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06/03/2017
Wikipedia Vandal Early Detection: from User Behavior to User Embedding
Wikipedia is the largest online encyclopedia that allows anyone to edit ...
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06/03/2017
Task-specific Word Identification from Short Texts Using a Convolutional Neural Network
Task-specific word identification aims to choose the task-related words ...
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02/28/2017
