Search Results for author: Ercan E. Kuruoglu

Found 7 papers, 3 papers with code

Thompson Sampling on Asymmetric $α$-Stable Bandits

no code implementations19 Mar 2022 Zhendong Shi, Ercan E. Kuruoglu, Xiaoli Wei

In algorithm optimization in reinforcement learning, how to deal with the exploration-exploitation dilemma is particularly important.

reinforcement-learning reinforcement Learning +1

Adaptive Sign Algorithm for Graph Signal Processing

no code implementations15 Jan 2022 Yi Yan, Ercan E. Kuruoglu, Mustafa A. Altınkaya

Recently introduced graph adaptive least mean squares algorithm is unstable under non-Gaussian impulsive noise and has high computational complexity.

PAC-Bayes Information Bottleneck

1 code implementation ICLR 2022 Zifeng Wang, Shao-Lun Huang, Ercan E. Kuruoglu, Jimeng Sun, Xi Chen, Yefeng Zheng

Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB.

Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback

1 code implementation NeurIPS 2020 Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan E. Kuruoglu, Yefeng Zheng

Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommender systems.

Recommendation Systems

A Generalized Gaussian Extension to the Rician Distribution for SAR Image Modeling

1 code implementation15 Jun 2020 Oktay Karakuş, Ercan E. Kuruoglu, Alin Achim

In this paper, we present a novel statistical model, $\textit{the generalized-Gaussian-Rician}$ (GG-Rician) distribution, for the characterization of synthetic aperture radar (SAR) images.

Black-Box Decision based Adversarial Attack with Symmetric $α$-stable Distribution

no code implementations11 Apr 2019 Vignesh Srinivasan, Ercan E. Kuruoglu, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima

Many existing methods employ Gaussian random variables for exploring the data space to find the most adversarial (for attacking) or least adversarial (for defense) point.

Adversarial Attack

Stable Graphical Models

no code implementations16 Apr 2014 Navodit Misra, Ercan E. Kuruoglu

Stable random variables are motivated by the central limit theorem for densities with (potentially) unbounded variance and can be thought of as natural generalizations of the Gaussian distribution to skewed and heavy-tailed phenomenon.

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