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Metric-Free Individual Fairness with Cooperative Contextual Bandits
Data mining algorithms are increasingly used in automated decision makin...
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FedAT: A Communication-Efficient Federated Learning Method with Asynchronous Tiers under Non-IID Data
Federated learning (FL) involves training a model over massive distribut...
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Learning Smooth and Fair Representations
Organizations that own data face increasing legal liability for its disc...
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Common-Knowledge Concept Recognition for SEVA
We build a common-knowledge concept recognition system for a Systems Eng...
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FineHand: Learning Hand Shapes for American Sign Language Recognition
American Sign Language recognition is a difficult gesture recognition pr...
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Unsupervised and Interpretable Domain Adaptation to Rapidly Filter Social Web Data for Emergency Services
During the onset of a disaster event, filtering relevant information fro...
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Diversity-Based Generalization for Neural Unsupervised Text Classification under Domain Shift
Domain adaptation approaches seek to learn from a source domain and gene...
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Academic Performance Estimation with Attention-based Graph Convolutional Networks
Student's academic performance prediction empowers educational technolog...
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Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction
Forecasting influenza-like illness (ILI) is of prime importance to epide...
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Low Rank Factorization for Compact Multi-Head Self-Attention
Effective representation learning from text has been an active area of r...
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Asynchronous Online Federated Learning for Edge Devices
Federated learning (FL) is a machine learning paradigm where a shared ce...
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Sign Language Recognition Analysis using Multimodal Data
Voice-controlled personal and home assistants (such as the Amazon Echo a...
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Federated Multi-task Hierarchical Attention Model for Sensor Analytics
Sensors are an integral part of modern Internet of Things (IoT) applicat...
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Multi-Differential Fairness Auditor for Black Box Classifiers
Machine learning algorithms are increasingly involved in sensitive decis...
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Reliable Deep Grade Prediction with Uncertainty Estimation
Currently, college-going students are taking longer to graduate than the...
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ALE: Additive Latent Effect Models for Grade Prediction
The past decade has seen a growth in the development and deployment of e...
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Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning
Multi-task learning (MTL) is a supervised learning paradigm in which the...
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Embedding Feature Selection for Large-scale Hierarchical Classification
Large-scale Hierarchical Classification (HC) involves datasets consistin...
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Inconsistent Node Flattening for Improving Top-down Hierarchical Classification
Large-scale classification of data where classes are structurally organi...
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Filter based Taxonomy Modification for Improving Hierarchical Classification
Hierarchical Classification (HC) is a supervised learning problem where ...
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