6 papers
Prior-Aligned 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 by meta-learning over synthetic data-generating processes -- making them high…
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…
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…
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…
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.…
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…