1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…
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…