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- Carnegie Mellon UniversityUS49 papers
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11 papers · 2 filters
Low-count Time Series Anomaly Detection
Philipp Renz, Kurt Cutajar, Niall Twomey +2
Low-count time series describe sparse or intermittent events, which are prevalent in large-scale online platforms that capture and monitor diverse data types. Several distinct chal…
When Fair Classification Meets Noisy Protected Attributes
Avijit Ghosh, Pablo Kvitca, Christo Wilson
The operationalization of algorithmic fairness comes with several practical challenges, not the least of which is the availability or reliability of protected attributes in dataset…
Balanced Filtering via Disclosure-Controlled Proxies
Siqi Deng, Emily Diana, Michael Kearns +1
We study the problem of collecting a cohort or set that is balanced with respect to sensitive groups when group membership is unavailable or prohibited from use at deployment time.…
Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices
Jing Zhu, Yuhang Zhou, Vassilis N. Ioannidis +4
While Graph Neural Networks (GNNs) are remarkably successful in a variety of high-impact applications, we demonstrate that, in link prediction, the common practices of including th…
Accelerator-Aware Training for Transducer-Based Speech Recognition
Suhaila M. Shakiah, Rupak Vignesh Swaminathan, Hieu Duy Nguyen +6
Machine learning model weights and activations are represented in full-precision during training. This leads to performance degradation in runtime when deployed on neural network a…
Attacking All Tasks at Once Using Adversarial Examples in Multi-Task Learning
Lijun Zhang, Xiao Liu, Kaleel Mahmood +2
Visual content understanding frequently relies on multi-task models to extract robust representations of a single visual input for multiple downstream tasks. However, in comparison…