3 papers
stat.ML2025
A Probabilistic Basis for Low-Rank Matrix Learning
Simon Segert, Nathan Wycoff
Low rank inference on matrices is widely conducted by optimizing a cost function augmented with a penalty proportional to the nuclear norm . However, despite t…
cs.LG2023
Flat Minima in Linear Estimation and an Extended Gauss Markov Theorem
Simon Segert
We consider the problem of linear estimation, and establish an extension of the Gauss-Markov theorem, in which the bias operator is allowed to be non-zero but bounded with respect…
cs.LG2023
Beyond Transformers for Function Learning
Simon Segert, Jonathan Cohen
The ability to learn and predict simple functions is a key aspect of human intelligence. Recent works have started to explore this ability using transformer architectures, however…