7 citations · 20 across the 12 of their papers we have counts for
6 papers · 1 filter
The Blind Spot Paradox: When Adaptive Classifiers Defeat Drift Detectors
Raphaël Minato, Fabrice Popineau, Arpad Rimmel +1
Monitoring concept drift from an adaptive classifier's error stream creates an operational conflict with the model's own update loop. When internal adaptation outpaces evidence acc…
Mitigating Convergence Collapse in Fixed-Target Anomaly Detectors via Kernel-Anchored Locality Regularization
José Lucas De Melo Costa, Fabrice Popineau, Arpad Rimmel +1
A family of tabular anomaly detectors trains a neural map toward a fixed target under squared-error loss and scores anomalies by the test-time residual; contraction matching, one-s…
Drive vs. Decay: On the Training Dynamics of Joint-Embedding Predictive Architectures
José Lucas De Melo Costa, Seong Woo Ahn, Fabrice Popineau +2
Joint-Embedding Predictive Architectures (JEPAs) are prone to representation collapse, typically mitigated through empirical heuristics. We develop an early-training stability theo…
Shifted S-templates and improved lower bounds for Schur numbers
Nils Bengone, Amine Brouk, Max Grinsztajn +4
We present an extension of the template-based approach for Schur numbers developed by Rowley. This new form of template construction, which we call shifted S-templates, was discove…
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…