21 citations · 37 across the 9 of their papers we have counts for
9 papers
MACROCAST: A Vintage-Consistent Time Series Foundation Model for Real-Time Macroeconomic Forecasting
Andrea Carriero, Davide Pettenuzzo, Shubhranshu Shekhar
We introduce MACROCAST, a lightweight Time Series Foundation Model (TSFM) for real-time macroeconomic forecasting. Existing TSFMs suffer from data leakage in two forms: temporal co…
Macroeconomic Forecasting with Large Language Models
Andrea Carriero, Davide Pettenuzzo, Shubhranshu Shekhar
This paper presents a comparative analysis evaluating the accuracy of Large Language Models (LLMs) against traditional macro time series forecasting approaches. In recent times, LL…
NetEffect: Discovery and Exploitation of Generalized Network Effects
Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo +1
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 no…
Unsupervised Machine Learning for Explainable Health Care Fraud Detection
Shubhranshu Shekhar, Jetson Leder-Luis, Leman Akoglu
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. A major concern i…
Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining
Jaemin Yoo, Meng-Chieh Lee, Shubhranshu Shekhar +1
How can we solve semi-supervised node classification in various graphs possibly with noisy features and structures? Graph neural networks (GNNs) have succeeded in many graph mining…
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series
Shubhranshu Shekhar, Dhivya Eswaran, Bryan Hooi +3
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? Early decision-ma…
gen2Out: Detecting and Ranking Generalized Anomalies
Meng-Chieh Lee, Shubhranshu Shekhar, Christos Faloutsos +2
In a cloud of m-dimensional data points, how would we spot, as well as rank, both single-point- as well as group- anomalies? We are the first to generalize anomaly detection in two…
FairOD: Fairness-aware Outlier Detection
Shubhranshu Shekhar, Neil Shah, Leman Akoglu
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. However, when being a minority (as de…
Incorporating Privileged Information to Unsupervised Anomaly Detection
Shubhranshu Shekhar, Leman Akoglu
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…