activity
20232026
most citedLarge language models can be zero-shot anomaly detectors for time series?

4 citations · 6 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2026

Model-Aware Data Cleaning for Tabular Foundation Models

Laure Berti-Equille

Tabular Foundation Models (TFMs) achieve state-of-the-art zero-shot accuracy on small tabular datasets, but their in-context learning assumes approximately clean inputs: real- worl…

cs.CV2024

Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures

Célia Blondin, Joris Guérin, Kelly Inagaki +2

Automated benthic image annotation is crucial to efficiently monitor and protect coral reefs against climate change. Current machine learning approaches fail to capture the hierarc…

cs.CL2024

Explingo: Explaining AI Predictions using Large Language Models

Alexandra Zytek, Sara Pido, Sarah Alnegheimish +2

Explanations of machine learning (ML) model predictions generated by Explainable AI (XAI) techniques such as SHAP are essential for people using ML outputs for decision-making. We…

cs.LG2024★ 4 cited

Large language models can be zero-shot anomaly detectors for time series?

Sarah Alnegheimish, Linh Nguyen, Laure Berti-Equille +1

Recent studies have shown the ability of large language models to perform a variety of tasks, including time series forecasting. The flexible nature of these models allows them to…

cs.CL2024★ 1 cited

Single Word Change is All You Need: Using LLMs to Create Synthetic Training Examples for Text Classifiers

Lei Xu, Sarah Alnegheimish, Laure Berti-Equille +2

In text classification, creating an adversarial example means subtly perturbing a few words in a sentence without changing its meaning, causing it to be misclassified by a classifi…

cs.LG2023

Pyreal: A Framework for Interpretable ML Explanations

Alexandra Zytek, Wei-En Wang, Dongyu Liu +2

Users in many domains use machine learning (ML) predictions to help them make decisions. Effective ML-based decision-making often requires explanations of ML models and their predi…

cs.LG2023★ 1 cited

OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User

Sarah Alnegheimish, Laure Berti-Equille, Kalyan Veeramachaneni

Time series anomaly detection is a vital task in many domains, including patient monitoring in healthcare, forecasting in finance, and predictive maintenance in energy industries.…