2 citations · 3 across the 6 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026★ 1 cited
Interpretable Tabular Foundation Models via In-Context Kernel Regression
Ratmir Miftachov, Bruno Charron, Simon Valentin
Tabular foundation models like TabPFN and TabICL achieve state-of-the-art performance through in-context learning, yet their architectures remain fundamentally opaque. We introduce…
cs.LG2026
High-Dimensional Search, Low-Dimensional Solution: Decoupling Optimization from Representation
Yusuf Kalyoncuoglu, Ratmir Miftachov
State-of-the-art models rely on massive widths despite exhibiting low Intrinsic Dimension (ID). We posit that this redundancy serves the non-convex optimization search rather than…