1 citations · 1 across the 2 of their papers we have counts for
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Knowledge-Informed Local Causal Discovery of Optimal Adjustment Sets
Seong Woo Ahn, Alessandro Leite, José Lucas De Melo Costa +3
Local causal discovery is a scalable alternative to global structure learning. However, it can struggle to identify valid adjustment sets in data-scarce settings because of finite-…
High Performance, Low Reliability: Uncertainty Benchmarking for Tabular Foundation Models
José Lucas De Melo Costa, Fabrice Popineau, Arpad Rimmel +1
Recent Tabular Foundation Models (TFMs) have demonstrated state-of-the-art predictive performance, often surpassing Gradient-Boosted Decision Trees (GBDTs). However, the trustworth…
T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data
Hugo Thimonier, José Lucas De Melo Costa, Fabrice Popineau +2
Self-supervision is often used for pre-training to foster performance on a downstream task by constructing meaningful representations of samples. Self-supervised learning (SSL) gen…
Retrieval Augmented Deep Anomaly Detection for Tabular Data
Hugo Thimonier, Fabrice Popineau, Arpad Rimmel +1
Deep learning for tabular data has garnered increasing attention in recent years, yet employing deep models for structured data remains challenging. While these models excel with u…
Beyond Individual Input for Deep Anomaly Detection on Tabular Data
Hugo Thimonier, Fabrice Popineau, Arpad Rimmel +1
Anomaly detection is vital in many domains, such as finance, healthcare, and cybersecurity. In this paper, we propose a novel deep anomaly detection method for tabular data that le…