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Found 7 papers, 4 papers with code
NetEffect: Discovery and Exploitation of Generalized Network Effects
Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects (GNE) or not, (b) estimate GNE to explain the interrelations among node classes, and (c) exploit GNE efficiently to improve the performance on downstream tasks?
Unsupervised Machine Learning for Explainable Health Care Fraud Detection
The US federal government spends more than a trillion dollars per year on health care, largely provided by private third parties and reimbursed by the government.
Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining
Graph neural networks (GNNs) have succeeded in many graph mining tasks, but their generalizability to various graph scenarios is limited due to the difficulty of training, hyperparameter tuning, and the selection of a model itself.
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series
Given a cardiac-arrest patient being monitored in the ICU (intensive care unit) for brain activity, how can we predict their health outcomes as early as possible?
gen2Out: Detecting and Ranking Generalized Anomalies
Our main contribution is the gen2Out algorithm, that has the following desirable properties: (a) Principled and Sound anomaly scoring that obeys the axioms for detectors, (b) Doubly-general in that it detects, as well as ranks generalized anomaly -- both point- and group-anomalies, (c) Scalable, it is fast and scalable, linear on input size.
FairOD: Fairness-aware Outlier Detection
Fairness and Outlier Detection (OD) are closely related, as it is exactly the goal of OD to spot rare, minority samples in a given population.
Incorporating Privileged Information to Unsupervised Anomaly Detection
We introduce a new unsupervised anomaly detection ensemble called SPI which can harness privileged information - data available only for training examples but not for (future) test examples.
