9 papers
Brownian Kernel Ladders
Mahdi Mohammadigohari, Giuseppe Di Fatta, Giuseppe Nicosia +1
We introduce Brownian kernel ladders (BKLs), a recursive hierarchy of integral reproducing kernel Hilbert spaces built from linear functionals by repeatedly integrating Brownian pu…
On the Koopman-Based Generalization Bounds for Multi-Task Deep Learning
Mahdi Mohammadigohari, Giuseppe Di Fatta, Giuseppe Nicosia +1
The paper establishes generalization bounds for multitask deep neural networks using operator-theoretic techniques. The authors propose a tighter bound than those derived from conv…
Operator-Based Generalization Bound for Deep Learning: Insights on Multi-Task Learning
Mahdi Mohammadigohari, Giuseppe Di Fatta, Giuseppe Nicosia +1
This paper presents novel generalization bounds for vector-valued neural networks and deep kernel methods, focusing on multi-task learning through an operator-theoretic framework.…
Multi-Task Learning Based on Support Vector Machines and Twin Support Vector Machines: A Comprehensive Survey
Fatemeh Bazikar, Hossein Moosaei, Atefeh Hemmati +1
Multi-task learning (MTL) enables simultaneous training across related tasks, leveraging shared information to improve generalization, efficiency, and robustness, especially in dat…
Diagonal Frobenius Number via Gomory's Relaxation and Discrepancy
Dmitry Gribanov, Dmitry Malyshev, Panos Pardalos
For a matrix of rank , the diagonal Frobenius number is defined as the minimum , such that, for any $b \in \text{sp…
Delta-modular ILP Problems of Bounded Codimension, Discrepancy, and Convolution (new version)
M. Cherniavskii, D. Gribanov, D. Malyshev +1
For integers and a cost vector , we study two fundamental integer linear programming (ILP) problems: \[ \text{(Standard Form)} \quad \max\bigl\{c^\top x \co…