Zobrazeno 1 - 10
of 993
pro vyhledávání: '"A Livanos"'
Autor:
Livanos, Vasilis, Mehta, Ruta
The I.I.D. Prophet Inequality is a fundamental problem where, given $n$ independent random variables $X_1,\dots,X_n$ drawn from a known distribution $\mathcal{D}$, one has to decide at every step $i$ whether to stop and accept $X_i$ or discard it for
Externí odkaz:
http://arxiv.org/abs/2411.19851
The Matroid Secretary Problem (MSP) is one of the most prominent settings for online resource allocation and optimal stopping. A decision-maker is presented with a ground set of elements $E$ revealed sequentially and in random order. Upon arrival, an
Externí odkaz:
http://arxiv.org/abs/2411.12069
Autor:
Livanos, Michael, Davidson, Ian
Deep anomaly detection (AD) is perhaps the most controversial of data analytic tasks as it identifies entities that are then specifically targeted for further investigation or exclusion. Also controversial is the application of AI to facial imaging d
Externí odkaz:
http://arxiv.org/abs/2407.19646
Over the past two decades, significant strides have been made in stochastic problems such as revenue-optimal auction design and prophet inequalities, traditionally modeled with $n$ independent random variables to represent the values of $n$ items. Ho
Externí odkaz:
http://arxiv.org/abs/2406.05077
In the classical prophet inequality settings, a gambler is given a sequence of $n$ random variables $X_1, \dots, X_n$, taken from known distributions, observes their values in this (potentially adversarial) order, and select one of them, immediately
Externí odkaz:
http://arxiv.org/abs/2404.11853
Autor:
Livanos, Michael, Davidson, Ian
Deep learning is extensively used in many areas of data mining as a black-box method with impressive results. However, understanding the core mechanism of how deep learning makes predictions is a relatively understudied problem. Here we explore the n
Externí odkaz:
http://arxiv.org/abs/2403.18278
Knowledge distillation is a simple but powerful way to transfer knowledge between a teacher model to a student model. Existing work suffers from at least one of the following key limitations in terms of direction and scope of transfer which restrict
Externí odkaz:
http://arxiv.org/abs/2402.05942
Explainable AI (XAI) is an important developing area but remains relatively understudied for clustering. We propose an explainable-by-design clustering approach that not only finds clusters but also exemplars to explain each cluster. The use of exemp
Externí odkaz:
http://arxiv.org/abs/2209.09670
Autor:
Livanos, Vasilis, Mehta, Ruta
Prophet inequalities for rewards maximization are fundamental to optimal stopping theory with extensive applications to mechanism design and online optimization. We study the \emph{cost minimization} counterpart of the classical prophet inequality: a
Externí odkaz:
http://arxiv.org/abs/2209.07988
Autor:
Chekuri, Chandra, Livanos, Vasilis
Publikováno v:
In Theoretical Computer Science 1 December 2024 1019
