Zobrazeno 1 - 10
of 209
pro vyhledávání: '"Medina, Andres"'
Autor:
Busa-Fekete, Róbert István, Dick, Travis, Gentile, Claudio, Medina, Andrés Muñoz, Smith, Adam, Swanberg, Marika
We propose reconstruction advantage measures to audit label privatization mechanisms. A reconstruction advantage measure quantifies the increase in an attacker's ability to infer the true label of an unlabeled example when provided with a private ver
Externí odkaz:
http://arxiv.org/abs/2406.02797
Unsupervised pre-training is a common step in developing computer vision models and large language models. In this setting, the absence of labels requires the use of similarity-based loss functions, such as contrastive loss, that favor minimizing the
Externí odkaz:
http://arxiv.org/abs/2310.03104
New regulations and increased awareness of data privacy have led to the deployment of new and more efficient differentially private mechanisms across public institutions and industries. Ensuring the correctness of these mechanisms is therefore crucia
Externí odkaz:
http://arxiv.org/abs/2307.05608
This work aims to deal with the optimal allocation instability problem of Markowitz's modern portfolio theory in high dimensionality. We propose a combined strategy that considers covariance matrix estimators from Random Matrix Theory~(RMT) and the m
Externí odkaz:
http://arxiv.org/abs/2306.05667
Autor:
Bravo-Hermsdorff, Gecia, Busa-Fekete, Róbert, Ghavamzadeh, Mohammad, Medina, Andres Muñoz, Syed, Umar
Modern statistical estimation is often performed in a distributed setting where each sample belongs to a single user who shares their data with a central server. Users are typically concerned with preserving the privacy of their samples, and also wit
Externí odkaz:
http://arxiv.org/abs/2305.07751
Autor:
Carey, CJ, Dick, Travis, Epasto, Alessandro, Javanmard, Adel, Karlin, Josh, Kumar, Shankar, Medina, Andres Munoz, Mirrokni, Vahab, Nunes, Gabriel Henrique, Vassilvitskii, Sergei, Zhong, Peilin
Compact user representations (such as embeddings) form the backbone of personalization services. In this work, we present a new theoretical framework to measure re-identification risk in such user representations. Our framework, based on hypothesis t
Externí odkaz:
http://arxiv.org/abs/2304.07210
Autor:
Busa-Fekete, Robert Istvan, Choi, Heejin, Dick, Travis, Gentile, Claudio, medina, Andres Munoz
We consider the problem of Learning from Label Proportions (LLP), a weakly supervised classification setup where instances are grouped into "bags", and only the frequency of class labels at each bag is available. Albeit, the objective of the learner
Externí odkaz:
http://arxiv.org/abs/2302.03115
Autor:
Epasto, Alessandro, Mao, Jieming, Medina, Andres Munoz, Mirrokni, Vahab, Vassilvitskii, Sergei, Zhong, Peilin
The streaming model of computation is a popular approach for working with large-scale data. In this setting, there is a stream of items and the goal is to compute the desired quantities (usually data statistics) while making a single pass through the
Externí odkaz:
http://arxiv.org/abs/2301.05605
We investigate block diagonal and hierarchical nested stochastic multivariate Gaussian models by studying their sample cross-correlation matrix on high dimensions. By performing numerical simulations, we compare a filtered sample cross-correlation wi
Externí odkaz:
http://arxiv.org/abs/2212.14650
Autor:
Medina, Andres Alberto
The origin of cosmic rays has been an open problem for over a century. By measuring and modeling the energy spectrum and mass composition we can provide information towards solving this problem. The energy spectrum in particular has several features