Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Yıldırım, Sinan"'
Autor:
Aydin, Soner, Yildirim, Sinan
We propose a novel Bayesian approach for the adaptive and online estimation of the frequency distribution of a finite number of categories under the local differential privacy (LDP) framework. The proposed algorithm performs Bayesian parameter estima
Externí odkaz:
http://arxiv.org/abs/2405.07020
Injecting heavy-tailed noise to the iterates of stochastic gradient descent (SGD) has received increasing attention over the past few years. While various theoretical properties of the resulting algorithm have been analyzed mainly from learning theor
Externí odkaz:
http://arxiv.org/abs/2403.02051
We propose a novel Bayesian inference framework for distributed differentially private linear regression. We consider a distributed setting where multiple parties hold parts of the data and share certain summary statistics of their portions in privac
Externí odkaz:
http://arxiv.org/abs/2301.13778
Autor:
Yıldırım, Sinan
We propose a novel online and adaptive truncation method for differentially private Bayesian online estimation of a static parameter regarding a population. We assume that sensitive information from individuals is collected sequentially and the infer
Externí odkaz:
http://arxiv.org/abs/2301.08202
Autor:
Aghaei, Vahid Tavakol, Ağababaoğlu, Arda, Bawo, Biram, Naseradinmousavi, Peiman, Yıldırım, Sinan, Yeşilyurt, Serhat, Onat, Ahmet
This study focuses on the numerical analysis and optimal control of vertical-axis wind turbines (VAWT) using Bayesian reinforcement learning (RL). We specifically address small-scale wind turbines, which are well-suited to local and compact productio
Externí odkaz:
http://arxiv.org/abs/2209.03485
Autor:
Alparslan, Baris, Yildirim, Sinan
Publikováno v:
Statistis and Computing, Aug 2022, Vol:32 Article no:66
This paper concerns differentially private Bayesian estimation of the parameters of a population distribution, when a statistic of a sample from that population is shared in noise to provide differential privacy. This work mainly addresses two proble
Externí odkaz:
http://arxiv.org/abs/2203.13377
We propose a new regression algorithm that learns from a set of input-output pairs. Our algorithm is designed for populations where the relation between the input variables and the output variable exhibits a heterogeneous behavior across the predicto
Externí odkaz:
http://arxiv.org/abs/2112.06251
This study examines a resource-sharing problem involving multiple parties that agree to use a set of capacities together. We start with modeling the whole problem as a mathematical program, where all parties are required to exchange information to ob
Externí odkaz:
http://arxiv.org/abs/2110.10498
Markov chain Monte Carlo (MCMC) methods to sample from a probability distribution $\pi$ defined on a space $(\Theta,\mathcal{T})$ consist of the simulation of realisations of Markov chains $\{\theta_{n},n\geq1\}$ of invariant distribution $\pi$ and s
Externí odkaz:
http://arxiv.org/abs/2101.01253
Publikováno v:
SIAM Journal on Optimization 2022 32:2, 795-821
We present two classes of differentially private optimization algorithms derived from the well-known accelerated first-order methods. The first algorithm is inspired by Polyak's heavy ball method and employs a smoothing approach to decrease the accum
Externí odkaz:
http://arxiv.org/abs/2008.01989