In "Dictionary Learning" one tries to recover incoherent matrices $A^* \in \mathbb{R}^{n \times h}$ (typically overcomplete and whose columns are ass…

Innovative auction methods can be exploited to increase profits, with Shubik's famous "dollar auction" perhaps being the most widely known example. R…

This paper considers the problem of completing a matrix with many missing entries under the assumption that the columns of the matrix belong to a uni…

Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distort…

We study a new notion of graph centrality based on absorbing random walks. Given a graph $G=(V,E)$ and a set of query nodes $Q\subseteq V$, we aim to…

In many applications, e.g., recommender systems and traffic monitoring, the data comes in the form of a matrix that is only partially observed and lo…

This paper addresses the problem of scheduling tasks with different criticality levels in the presence of I/O requests. In mixed-criticality scheduli…

We introduce the notion of sink-stable sets of a digraph and prove a min-max formula for the maximum cardinality of the union of k sink-stable sets. …

We propose theoretical and empirical improvements for two-stage hashing methods. We first provide a theoretical analysis on the quality of the binary…

Many applications have service requirements that are not easily met by existing operating systems. Real-time and security-critical tasks, for example…

It has been empirically observed that the flatness of minima obtained from training deep networks seems to correlate with better generalization. Howe…

Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulat…

Foreground object segmentation is a critical step for many image analysis tasks. While automated methods can produce high-quality results, their fail…

Privacy concerns in outsourced cloud databases have become more and more important recently and many efficient and scalable query processing methods …

Supervised hashing methods are widely-used for nearest neighbor search in computer vision applications. Most state-of-the-art supervised hashing appr…

We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambigu…

We study the problem of supervised learning for both binary and multiclass classification from a unified geometric perspective. In particular, we pro…

We propose a Bayesian approach for recursively estimating the classifier weights in online learning of a classifier ensemble. In contrast with past m…

Given a computable probability measure P over natural numbers or infinite binary sequences, there is no method that can produce an arbitrarily large …

Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete sou…

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