Soysal Degirmenci

Soysal Degirmenci, Chris Jones

Amazon and other e-commerce sites must employ mechanisms to protect their millions of customers from fraud, such as unauthorized use of credit cards. One such mechanism is order fraud evaluation, where systems evaluate orders for fraud risk, and either "pass" the order, or take an action to mitigate high risk. Order fraud evaluation systems typically use binary classification models that distinguish fraudulent and legitimate orders, to assess risk and take action. We seek to devise a system that considers both financial losses of fraud and long-term customer satisfaction, which may be impaired when incorrect actions are applied to legitimate customers. We propose that taking actions to optimize long-term impact can be formulated as a Reinforcement Learning (RL) problem. Standard RL methods require online interaction with an environment to learn, but this is not desirable in high-stakes applications like order fraud evaluation. Offline RL algorithms learn from logged data collected from the environment, without the need for online interaction, making them suitable for our use case. We show that offline RL methods outperform traditional binary classification solutions in SimStore, a simplified e-commerce simulation that incorporates order fraud risk. We also propose a novel approach to training offline RL policies that adds a new loss term during training, to better align policy exploration with taking correct actions.

S. Degirmenci, Joseph A. O'Sullivan, David G. Politte

Three-dimensional x-ray CT image reconstruction in baggage scanning in security applications is an important research field. The variety of materials to be reconstructed is broader than medical x-ray imaging. Presence of high attenuating materials such as metal may cause artifacts if analytical reconstruction methods are used. Statistical modeling and the resultant iterative algorithms are known to reduce these artifacts and present good quantitative accuracy in estimates of linear attenuation coefficients. However, iterative algorithms may require computations in order to achieve quantitatively accurate results. For the case of baggage scanning, in order to provide fast accurate inspection throughput, they must be accelerated drastically. There are many approaches proposed in the literature to increase speed of convergence. This paper presents a new method that estimates the wavelet coefficients of the images in the discrete wavelet transform domain instead of the image space itself. Initially, surrogate functions are created around approximation coefficients only. As the iterations proceed, the wavelet tree on which the updates are made is expanded based on a criterion and detail coefficients at each level are updated and the tree is expanded this way. For example, in the smooth regions of the image the detail coefficients are not updated while the coefficients that represent the high-frequency component around edges are being updated, thus saving time by focusing computations where they are needed. This approach is implemented on real data from a SureScan (TM) x1000 Explosive Detection System and compared to straightforward implementation of the unregularized alternating minimization of O'Sullivan and Benac [1].

Yan Kaganovsky, Shaobo Han, Soysal Degirmenci et al.

We propose a globally convergent alternating minimization (AM) algorithm for image reconstruction in transmission tomography, which extends automatic relevance determination (ARD) to Poisson noise models with Beer's law. The algorithm promotes solutions that are sparse in the pixel/voxel-differences domain by introducing additional latent variables, one for each pixel/voxel, and then learning these variables from the data using a hierarchical Bayesian model. Importantly, the proposed AM algorithm is free of any tuning parameters with image quality comparable to standard penalized likelihood methods. Our algorithm exploits optimization transfer principles which reduce the problem into parallel 1D optimization tasks (one for each pixel/voxel), making the algorithm feasible for large-scale problems. This approach considerably reduces the computational bottleneck of ARD associated with the posterior variances. Positivity constraints inherent in transmission tomography problems are also enforced. We demonstrate the performance of the proposed algorithm for x-ray computed tomography using synthetic and real-world datasets. The algorithm is shown to have much better performance than prior ARD algorithms based on approximate Gaussian noise models, even for high photon flux.