The problem of learning-to-learn (LTL) or meta-learning is gaining increasing attention due
to recent empirical evidence of its effectiveness in applications. The goal addressed in LTL …

We consider the problem of transfer learning in an online setting. Different tasks are
presented sequentially and processed by a within-task algorithm. We propose a lifelong …

In learning-to-learn the goal is to infer a learning algorithm that works well on a class of tasks
sampled from an unknown meta distribution. In contrast to previous work on batch learning …

We focus on the completion of a (possibly) low-rank matrix with binary entries, the so-called
1-bit matrix completion problem. Our approach relies on tools from machine learning theory …

We give an online algorithm and prove novel mistake and regret bounds for online binary
matrix completion with side information. The mistake bounds we prove are of the form\tilde …

The problem of estimating a matrix based on a set of observed entries is commonly referred
to as the matrix completion problem. In this work, we specifically address the scenario of …

We consider a challenging multi-label classification problem where both feature matrix $\X $
and label matrix $\Y $ have missing entries. An existing method concatenated $\X $ and $\Y …

This study investigates the misclassification excess risk bound in the context of 1‐bit matrix
completion, a significant problem in machine learning involving the recovery of an unknown …

We consider online linear optimization over symmetric positive semi-definite matrices, which
has various applications including the online collaborative filtering. The problem is …

In this paper, we provide a rigorous theoretical investigation of an online learning version of
the Facility Location problem which is motivated by emerging problems in real-world …