arXiv subjects

Sebastian Vogt

Publications and source records attributed to Sebastian Vogt.

5 recordsLinked to original sources

arXiv2026

Nonparametric Kernel Clustering with Bandit Feedback

Clustering with bandit feedback refers to the problem of partitioning a set of items, where the clustering algorithm can sequentially query the items to receive noisy observations. The problem is formally posed as the task of partitioning the arms of an N-armed stochastic bandit according to their underlying distributions, grouping two arms together if and only if they share the same distribution, using samples collected sequentially and adaptively. This setting has gained attention in recent years due to its applicability in recommendation systems and crowdsourcing. Existing works on clustering with bandit feedback rely on a strong assumption that the underlying distributions are sub-Gaussian. As a consequence, the existing methods mainly cover settings with linearly-separable clusters, which has little practical relevance. We introduce a framework of ``nonparametric clustering with bandit feedback'', where the underlying arm distributions are not constrained to any parametric, and hence, it is applicable for active clustering of real-world datasets. We adopt a kernel-based approach, which allows us to reformulate the nonparametric problem as the task of clustering the arms according to their kernel mean embeddings in a reproducing kernel Hilbert space (RKHS). Building on this formulation, we introduce the KABC algorithm with theoretical correctness guarantees and analyze its sampling budget. We introduce a notion of signal-to-noise ratio for this problem that depends on the maximum mean discrepancy (MMD) between the arm distributions and on their variance in the RKHS. Our algorithm is adaptive to this unknown quantity: it does not require it as an input yet achieves instance-dependent guarantees.

stat.ML↗

arXiv2023

Samplable Anonymous Aggregation for Private Federated Data Analysis

We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private algorithms require little trust but are (provably) limited in their utility. Centrally differentially private algorithms can allow significantly better utility but require a trusted curator. This gap has led to significant interest in the design and implementation of simple cryptographic primitives, that can allow central-like utility guarantees without having to trust a central server. Our first contribution is to propose a new primitive that allows for efficient implementation of several commonly used algorithms, and allows for privacy accounting that is close to that in the central setting without requiring the strong trust assumptions it entails. {\em Shuffling} and {\em aggregation} primitives that have been proposed in earlier works enable this for some algorithms, but have significant limitations as primitives. We propose a {\em Samplable Anonymous Aggregation} primitive, which computes an aggregate over a random subset of the inputs and show that it leads to better privacy-utility trade-offs for various fundamental tasks. Secondly, we propose a system architecture that implements this primitive and perform a security analysis of the proposed system. Our design combines additive secret-sharing with anonymization and authentication infrastructures.

cs.CR↗

arXiv2020

C-Arm Non-Circular Orbits: Geometric Calibration, Image Quality, and Avoidance of Metal Artifacts

Metal artifacts present a frequent challenge to cone-beam CT (CBCT) in image-guided surgery, obscuring visualization of metal instruments and adjacent anatomy. Recent advances in mobile C-arm systems have enabled 3D imaging capacity with non-circular orbits. We extend a previously proposed metal artifacts avoidance (MAA) method to reduce the influence of metal artifacts by prospectively defining a non-circular orbit that avoids metal-induced biases in projection domain. Accurate geometric calibration is an important challenge to accurate 3D image reconstruction for such orbits. We investigate the performance of interpolation-based calibration from a library of circular orbits for any non-circular orbit. We apply the method to non-circular scans acquired for MAA, which involves: (i) coarse 3D localization of metal objects via only two scout views using an end-to-end trained neural network; (ii) calculation of the metal-induced x-ray spectral shift for all possible views; and (iii) identification of the non-circular orbit that minimizes the variations in spectral shift. Non-circular orbits with interpolation-based geometric calibration yielded reasonably accurate 3D image reconstruction. The end-to-end neural network accurately localized metal implants with just two scout views even in complex anatomical scenes, improving Dice coefficient by ~42% compared to a more conventional cascade of separately trained U-nets. In a spine phantom with pedicle screw instrumentation, non-circular orbits identified by the MAA method reduced the magnitude of metal "blomming" artifacts (apparent width of the screw shaft) in CBCT reconstructions by ~70%. The proposed imaging and calibration methods present a practical means to improve image quality in mobile C-arm CBCT by identifying non-circular scan protocols that improve sampling and reduce metal-induced biases in the projection data.

physics.med-ph↗

arXiv2020

Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Tools

Testing new, innovative technologies is a crucial task for safety and acceptance. But how can new systems be tested if no historical real-world data exist? Simulation provides an answer to this important question. Classical simulation tools such as event-based simulation are well accepted. But most of these established simulation models require the specification of many parameters. Furthermore, simulation runs, e.g., CFD simulations, are very time consuming. Generative Adversarial Networks (GANs) are powerful tools for generating new data for a variety of tasks. Currently, their most frequent application domain is image generation. This article investigates the applicability of GANs for imitating simulations. We are comparing the simulation output of a technical system with the output of a GAN. To exemplify this approach, a well-known multi-car elevator system simulator was chosen. Our study demonstrates the feasibility of this approach. It also discusses pitfalls and technical problems that occurred during the implementation. Although we were able to show that in principle, GANs can be used as substitutes for expensive simulation runs, we also show that they cannot be used "out of the box". Fine tuning is needed. We present a proof-of-concept, which can serve as a starting point for further research.

stat.ML↗

arXiv2020

Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based ground-truth

Purpose: To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. Materials and Methods: We retrospectively selected a cohort of 86 COVID-19 patients (with positive RT-PCR), from March-May 2020 at a tertiary hospital in the northeastern USA, who underwent chest CT and CXR within 48 hrs. The ground truth volumetric percentage of COVID-19 related AD (POv) was established by manual AD segmentation on CT. The resulting 3D masks were projected into 2D anterior-posterior digitally reconstructed radiographs (DRR) to compute area-based AD percentage (POa). A convolutional neural network (CNN) was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD and quantifying POa on CXR. CNN POa results were compared to POa quantified on CXR by two expert readers and to the POv ground-truth, by computing correlations and mean absolute errors. Results: Bootstrap mean absolute error (MAE) and correlations between POa and POv were 11.98% [11.05%-12.47%] and 0.77 [0.70-0.82] for average of expert readers, and 9.56%-9.78% [8.83%-10.22%] and 0.78-0.81 [0.73-0.85] for the CNN, respectively. Conclusion: Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of airspace disease on CXR, in patients with positive RT-PCR for COVID-19.

eess.IV↗