46 citations · 69 across the 6 of their papers we have counts for
6 papers
TopRank+: A Refinement of TopRank Algorithm
Victor de la Pena, Haolin Zou
Online learning to rank is a core problem in machine learning. In Lattimore et al. (2018), a novel online learning algorithm was proposed based on topological sorting. In the paper…
How to Find New Characteristic-Dependent Linear Rank Inequalities using Binary Matrices as a Guide
Victor Peña, Humberto Sarria
In Linear Algebra over finite fields, a characteristic-dependent linear rank inequality is a linear inequality that holds by ranks of subspaces of a vector space over a finite fiel…
Characteristic-Dependent Linear Rank Inequalities via Complementary Vector Spaces
Victor Pena, Humberto Sarria
A characteristic-dependent linear rank inequality is a linear inequality that holds by ranks of subspaces of a vector space over a finite field of determined characteristic, and do…
Pseudo-maximization and self-normalized processes
Victor H. de la Peña, Michael J. Klass, Tze Leung Lai
Self-normalized processes are basic to many probabilistic and statistical studies. They arise naturally in the the study of stochastic integrals, martingale inequalities and limit…
Inverse problems for random walks on trees: network tomography
Victor de la Pena, Henryk Gzyl, Patrick McDonald
Let be a finite tree with root and associate to the internal vertices of a collection of transition probabilities for a simple nondegenerate Markov chain. Embedd in…
Self-normalized processes: exponential inequalities, moment bounds and iterated logarithm laws
Victor H. de la Pena, Michael J. Klass, Tze Leung Lai
Self-normalized processes arise naturally in statistical applications. Being unit free, they are not affected by scale changes. Moreover, self-normalization often eliminates or wea…