Search Results for author:
Found 16 papers, 7 papers with code
End-To-End Self-tuning Self-supervised Time Series Anomaly Detection
A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types of time series anomalies (spikes, discontinuities, trend shifts, etc.)
Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques
This paper investigates the transferability of debiasing techniques across different languages within multilingual models.
SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA
The performance of In-Context Learning depends heavily on the selection procedure of the supporting exemplars, particularly in the case of HybridQA, where considering the diversity of reasoning chains and the large size of the hybrid contexts becomes crucial.
INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks
We compare the results of various models to demonstrate the importance of capturing graph representation, temporal dependencies, and using a profit-driven methodology for evaluation.
Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors
IoT data is a central element in the successful digital transformation of agriculture.
Influencer Detection with Dynamic Graph Neural Networks
Leveraging network information for prediction tasks has become a common practice in many domains.
Prescriptive maintenance with causal machine learning
This work proposes to relax both assumptions by learning the effect of maintenance conditional on a machine's characteristics from observational data on similar machines using existing methodologies for causal inference.
A new perspective on classification: optimally allocating limited resources to uncertain tasks
A central problem in business concerns the optimal allocation of limited resources to a set of available tasks, where the payoff of these tasks is inherently uncertain.
Expert-driven Trace Clustering with Instance-level Constraints
Within the field of process mining, several different trace clustering approaches exist for partitioning traces or process instances into similar groups.
Social network analytics for supervised fraud detection in insurance
Finally, we combine these network features with the claim-specific features and build a supervised model with fraud in motor insurance as the target variable.
Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud
Fraud detection is to be acknowledged as an instance-dependent cost-sensitive classification problem, where the costs due to misclassification vary between instances, and requiring adapted approaches for learning a classification model.
Applications
Autoencoders for strategic decision support
However, few data-driven tools that support strategic decision-making are available.
robROSE: A robust approach for dealing with imbalanced data in fraud detection
Detecting fraud in such a highly imbalanced data set typically leads to predictions that favor the majority group, causing fraud to remain undetected.
The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics
In terms of profit, the best model is the one built with only calling behavior features.
Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective
This paper aims to facilitate this process for high-level tactical sales forecasts by comparing a large array of techniques for 35 times series that consist of both industry data from the Coca-Cola Company and publicly available datasets.
Profit Driven Decision Trees for Churn Prediction
Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base.
