20 citations · 70 across the 24 of their papers we have counts for
26 papers
Sequential Monte Carlo Sampling of Feasible Regions Represented by R-Functions
Sinan Yıldırım, Sergei Kucherenko
Feasible-region sampling and volume estimation become increasingly inefficient when constraints define only a small fraction of a convenient bounding domain. We combine R-functions…
Combining Homomorphic Encryption and Differential Privacy in Federated Learning for Model Inspection and Availability
Ceren Yıldırım, Kamer Kaya, Sinan Yıldırım +1
The increasing prevalence of decentralized data has led to a growing interest in federated learning, which enables collaborative model training without clients sharing their sensit…
The Quick and the Dead: Estimating Sparse-Matrix Permanents with Adaptive Work Filtering
Deniz Elbek, Yiğit Manafi, Zeynep Gürdal +2
Rasmussen's permanent estimator is a simple and unbiased estimator for the permanent of a binary matrix, but its practical performance can be limited by trajectories that terminate…
Bayesian Membership Privacy for Graph Neural Networks
Sinan Yıldırım, Megha Khosla
Existing privacy analyses for Graph Neural Networks (GNNs) largely inherit assumptions from non-graph settings, overlooking structural correlations and stochastic training-graph sa…
Multiple Jump MCMC: A Scalable Algorithm for Bayesian Inference on Binary Model Spaces
Lucas Vogels, Reza Mohammadi, Marit Schoonhoven +2
This article considers Bayesian model inference on binary model spaces. Binary model spaces are used by a large class of models, including graphical models, variable selection, mix…
Rényi Differential Privacy for Heavy-Tailed SDEs via Fractional Poincaré Inequalities
Benjamin Dupuis, Mert Gürbüzbalaban, Umut Şimşekli +3
Characterizing the differential privacy (DP) of learning algorithms has become a major challenge in recent years. In parallel, many studies suggested investigating the behavior of…
MCMC for Bayesian estimation of Differential Privacy from Membership Inference Attacks
Ceren Yildirim, Kamer Kaya, Sinan Yildirim +1
We propose a new framework for Bayesian estimation of differential privacy, incorporating evidence from multiple membership inference attacks (MIA). Bayesian estimation is carried…
Bayesian Frequency Estimation Under Local Differential Privacy With an Adaptive Randomized Response Mechanism
Soner Aydin, Sinan Yildirim
Frequency estimation plays a critical role in many applications involving personal and private categorical data. Such data are often collected sequentially over time, making it val…
Privacy of SGD under Gaussian or Heavy-Tailed Noise: Guarantees without Gradient Clipping
Umut Şimşekli, Mert Gürbüzbalaban, Sinan Yıldırım +1
The injection of heavy-tailed noise into the iterates of stochastic gradient descent (SGD) has garnered growing interest in recent years due to its theoretical and empirical benefi…
Differentially Private Online Bayesian Estimation With Adaptive Truncation
Sinan Yıldırım
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 sensitiv…
Differentially Private Distributed Bayesian Linear Regression with MCMC
Barış Alparslan, Sinan Yıldırım, Ş. İlker Birbil
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…
Energy Optimization of Wind Turbines via a Neural Control Policy Based on Reinforcement Learning Markov Chain Monte Carlo Algorithm
Vahid Tavakol Aghaei, Arda Ağababaoğlu, Biram Bawo +4
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-sca…
Statistic Selection and MCMC for Differentially Private Bayesian Estimation
Baris Alparslan, Sinan Yildirim
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…
Learning with Subset Stacking
Ş. İlker Birbil, Sinan Yıldırım, Samet Çopur +1
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…
Differentially Private Linear Optimization for Multi-Party Resource Sharing
Utku Karaca, Nursen Aydin, Sinan Yildirim +1
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…
Differentially Private Accelerated Optimization Algorithms
Nurdan Kuru, Ş. İlker Birbil, Mert Gurbuzbalaban +1
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 he…
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya Urns
Ali Taylan Cemgil, Mehmet Burak Kurutmaz, Sinan Yildirim +2
We introduce a dynamic generative model, Bayesian allocation model (BAM), which establishes explicit connections between nonnegative tensor factorization (NTF), graphical models of…
Scalable Monte Carlo inference for state-space models
Sinan Yıldırım, Christophe Andrieu, Arnaud Doucet
We present an original simulation-based method to estimate likelihood ratios efficiently for general state-space models. Our method relies on a novel use of the conditional Sequent…
Image Segmentation with Pseudo-marginal MCMC Sampling and Nonparametric Shape Priors
Ertunc Erdil, Sinan Yildirim, Tolga Tasdizen +1
In this paper, we propose an efficient pseudo-marginal Markov chain Monte Carlo (MCMC) sampling approach to draw samples from posterior shape distributions for image segmentation.…
On the utility of Metropolis-Hastings with asymmetric acceptance ratio
Christophe Andrieu, Arnaud Doucet, Sinan Yıldırım +1
The Metropolis-Hastings algorithm allows one to sample asymptotically from any probability distribution . There has been recently much work devoted to the development of variant…
MCMC Shape Sampling for Image Segmentation with Nonparametric Shape Priors
Ertunc Erdil, Sinan Yıldırım, Müjdat Çetin +1
Segmenting images of low quality or with missing data is a challenging problem. Integrating statistical prior information about the shapes to be segmented can improve the segmentat…
On the Use of Penalty MCMC for Differential Privacy
Sinan Yıldırım
We view the penalty algorithm of Ceperley and Dewing (1999), a Markov chain Monte Carlo (MCMC) algorithm for Bayesian inference, in the context of data privacy. Specifically, we st…
Bayesian tracking and parameter learning for non-linear multiple target tracking models
Lan Jiang, Sumeetpal S. Singh, Sinan Yıldırım
We propose a new Bayesian tracking and parameter learning algorithm for non-linear non-Gaussian multiple target tracking (MTT) models. We design a Markov chain Monte Carlo (MCMC) a…
An Online Expectation-Maximisation Algorithm for Nonnegative Matrix Factorisation Models
Sinan Yildirim, A. Taylan Cemgil, Sumeetpal S. Singh
In this paper we formulate the nonnegative matrix factorisation (NMF) problem as a maximum likelihood estimation problem for hidden Markov models and propose online expectation-max…