13 citations · 37 across the 15 of their papers we have counts for
17 papers
Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes
Fred Xu, Thomas Markovich, Florence Regol +1
Node-level graph anomaly detection (GAD) identifies nodes whose attributes and interactions deviate from dominant graph regularities. Existing GAD models encode normality and anoma…
A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion
Fred Xu, Thomas Markovich, Florence Regol +1
Reliable deployment of graph neural networks requires calibration, out-of-distribution (OOD) detection, and robustness to distribution shift, yet existing methods address these nee…
When to retrain a machine learning model
Regol Florence, Schwinn Leo, Sprague Kyle +2
A significant challenge in maintaining real-world machine learning models is responding to the continuous and unpredictable evolution of data. Most practitioners are faced with the…
Dynamic layer selection in decoder-only transformers
Theodore Glavas, Joud Chataoui, Florence Regol +4
The vast size of Large Language Models (LLMs) has prompted a search to optimize inference. One effective approach is dynamic inference, which adapts the architecture to the sample-…
Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE
Florence Regol, Joud Chataoui, Bertrand Charpentier +3
Machine learning models can solve complex tasks but often require significant computational resources during inference. This has led to the development of various post-training com…
Interacting Diffusion Processes for Event Sequence Forecasting
Mai Zeng, Florence Regol, Mark Coates
Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time intervals, but their sequential nature…