2 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
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…
EarlyStopping: Implicit Regularization for Iterative Learning Procedures in Python
Eric Ziebell, Ratmir Miftachov, Bernhard Stankewitz +1
Iterative learning procedures are ubiquitous in machine learning and modern statistics. Regularision is typically required to prevent inflating the expected loss of a procedure in…
Early Stopping for Regression Trees
Ratmir Miftachov, Markus Reiß
We develop early stopping rules for growing regression tree estimators. The fully data-driven stopping rule is based on monitoring the global residual norm. The best-first search a…
Risk Premia in the Bitcoin Market
Caio Almeida, Maria Grith, Ratmir Miftachov +1
We analyze the first and second moment risk premia in the Bitcoin market based on options and realized returns and contrast them to the premia embedded in the main US stock index m…
Shapley Curves: A Smoothing Perspective
Ratmir Miftachov, Georg Keilbar, Wolfgang Karl Härdle
This paper fills the limited statistical understanding of Shapley values as a variable importance measure from a nonparametric (or smoothing) perspective. We introduce population-l…