no code implementations • ICML 2020 • Kareem Aggour, Bülent Yener
This work considers the canonical polyadic decomposition (CPD) of tensors using proximally regularized sketched alternating least squares algorithms.
no code implementations • 31 May 2023 • Deniz Koyuncu, Alex Gittens, Bülent Yener, Moti Yung
Inference of causal structures from observational data is a key component of causal machine learning; in practice, this data may be incompletely observed.
no code implementations • 26 Feb 2022 • Deniz Koyuncu, Bülent Yener
One limitation of the most statistical/machine learning-based variable selection approaches is their inability to control the false selections.
no code implementations • 8 Jul 2021 • Daniel Park, Haidar Khan, Azer Khan, Alex Gittens, Bülent Yener
Adversarial examples pose a threat to deep neural network models in a variety of scenarios, from settings where the adversary has complete knowledge of the model in a "white box" setting and to the opposite in a "black box" setting.
no code implementations • 14 Nov 2020 • Zaid Bin Tariq, Arun Iyengar, Lara Marcuse, Hui Su, Bülent Yener
But these models require a considerable number of patient-specific seizures to be recorded for extracting the preictal and interictal EEG data for training a classifier.
no code implementations • 6 Nov 2020 • Daniel Park, Hannah Powers, Benji Prashker, Leland Liu, Bülent Yener
It is imperative to protect these devices as they become more prevalent in commercial and personal networks.
no code implementations • 6 Nov 2020 • Daniel Park, Bülent Yener
To fully understand the impact of adversarial examples on malware detection, we review practical attacks against malware classifiers that generate executable adversarial malware examples.
no code implementations • 27 Jul 2020 • Wufei Ma, Elizabeth Kautz, Arun Baskaran, Aritra Chowdhury, Vineet Joshi, Bülent Yener, Daniel Lewis
A binary alloy (uranium-molybdenum) that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions.
no code implementations • 13 Jun 2019 • Elizabeth Kautz, Wufei Ma, Saumyadeep Jana, Arun Devaraj, Vineet Joshi, Bülent Yener, Daniel Lewis
Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system.
no code implementations • ICLR 2020 • Haidar Khan, Daniel Park, Azer Khan, Bülent Yener
Adversarial examples pose a threat to deep neural network models in a variety of scenarios, from settings where the adversary has complete knowledge of the model and to the opposite "black box" setting.
no code implementations • 23 May 2019 • Haidar Khan, Lara Marcuse, Bülent Yener
In this work, we propose new objective functions to train deep neural network based density ratio estimators and apply it to a change point detection problem.
no code implementations • 9 Apr 2019 • Daniel Park, Haidar Khan, Bülent Yener
There has been an increased interest in the application of convolutional neural networks for image based malware classification, but the susceptibility of neural networks to adversarial examples allows malicious actors to evade classifiers.
no code implementations • 29 May 2018 • Haidar Khan, Lara Marcuse, Madeline Fields, Kalina Swann, Bülent Yener
Significance: We demonstrate that a robust set of features can be learned from scalp EEG that characterize the preictal state of focal seizures.