Search Results for author: Sinan Yildirim

Found 13 papers, 6 papers with code

Differentially Private Distributed Bayesian Linear Regression with MCMC

1 code implementation31 Jan 2023 Barış Alparslan, Sinan Yildirim, Ş. İlker Birbil

We develop a novel generative statistical model for privately shared statistics, which exploits a useful distributional relation between the summary statistics of linear regression.

Bayesian Inference Privacy Preserving +1

Differentially Private Online Bayesian Estimation With Adaptive Truncation

1 code implementation19 Jan 2023 Sinan Yildirim

We propose a novel online and adaptive truncation method for differentially private Bayesian online estimation of a static parameter regarding a population.

Privacy Preserving Thompson Sampling

Statistic Selection and MCMC for Differentially Private Bayesian Estimation

1 code implementation24 Mar 2022 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 to provide differential privacy.

Bayesian Inference

Learning with Subset Stacking

1 code implementation12 Dec 2021 S. İlker Birbil, Sinan Yildirim, Kaya Gökalp, M. Hakan Akyüz

We call this algorithm ``LEarning with Subset Stacking'' or LESS, due to its resemblance to the method of stacking regressors.

Differentially Private Accelerated Optimization Algorithms

1 code implementation5 Aug 2020 Nurdan Kuru, Ş. İlker Birbil, Mert Gurbuzbalaban, Sinan Yildirim

The first algorithm is inspired by Polyak's heavy ball method and employs a smoothing approach to decrease the accumulated noise on the gradient steps required for differential privacy.

Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs

no code implementations pproximateinference AABI Symposium 2019 Mehmet Burak Kurutmaz, Melih Barsbey, Ali Taylan Cemgil, Sinan Yildirim, Umut Şimşekli

We believe that the Bayesian approach to causal discovery both allows the rich methodology of Bayesian inference to be used in various difficult aspects of this problem and provides a unifying framework to causal discovery research.

Bayesian Inference Causal Discovery +1

Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya Urns

1 code implementation11 Mar 2019 Ali Taylan Cemgil, Mehmet Burak Kurutmaz, Sinan Yildirim, Melih Barsbey, Umut Simsekli

We introduce a dynamic generative model, Bayesian allocation model (BAM), which establishes explicit connections between nonnegative tensor factorization (NTF), graphical models of discrete probability distributions and their Bayesian extensions, and the topic models such as the latent Dirichlet allocation.

Model Selection Topic Models

Image Segmentation with Pseudo-marginal MCMC Sampling and Nonparametric Shape Priors

no code implementations3 Sep 2018 Ertunc Erdil, Sinan Yildirim, Tolga Tasdizen, Mujdat Cetin

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.

Image Segmentation Semantic Segmentation

MCMC Shape Sampling for Image Segmentation with Nonparametric Shape Priors

no code implementations CVPR 2016 Ertunc Erdil, Sinan Yildirim, Müjdat Çetin, Tolga Taşdizen

With a statistical view, addressing these issues would involve the problem of characterizing the posterior densities of the shapes of the objects to be segmented.

Image Segmentation Segmentation +1

Bayesian tracking and parameter learning for non-linear multiple target tracking models

no code implementations8 Oct 2014 Lan Jiang, Sumeetpal S. Singh, Sinan Yildirim

We propose a new Bayesian tracking and parameter learning algorithm for non-linear non-Gaussian multiple target tracking (MTT) models.

An Online Expectation-Maximisation Algorithm for Nonnegative Matrix Factorisation Models

no code implementations11 Jan 2014 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-maximisation (EM) algorithms to estimate the NMF and the other unknown static parameters.

Parameter Estimation in Hidden Markov Models with Intractable Likelihoods Using Sequential Monte Carlo

no code implementations17 Nov 2013 Sinan Yildirim, Sumeetpal Singh, Thomas Dean, Ajay Jasra

We propose sequential Monte Carlo based algorithms for maximum likelihood estimation of the static parameters in hidden Markov models with an intractable likelihood using ideas from approximate Bayesian computation.

Computation Methodology

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