Zobrazeno 1 - 10
of 9 102
pro vyhledávání: '"A. Stoian"'
Autor:
Khan, Salman, Teeti, Izzeddin, Alitappeh, Reza Javanmard, Stoian, Mihaela C., Giunchiglia, Eleonora, Singh, Gurkirt, Bradley, Andrew, Cuzzolin, Fabio
Autonomous Vehicle (AV) perception systems require more than simply seeing, via e.g., object detection or scene segmentation. They need a holistic understanding of what is happening within the scene for safe interaction with other road users. Few dat
Externí odkaz:
http://arxiv.org/abs/2411.01683
Autor:
Stoian, Mihail, van Renen, Alexander, Kobiolka, Jan, Kuo, Ping-Lin, Grabocka, Josif, Kipf, Andreas
The growing adoption of data lakes for managing relational data necessitates efficient, open storage formats that provide high scan performance and competitive compression ratios. While existing formats achieve fast scans through lightweight encoding
Externí odkaz:
http://arxiv.org/abs/2410.14066
Deep Generative Models (DGMs) have found application in computer vision for generating adversarial examples to test the robustness of machine learning (ML) systems. Extending these adversarial techniques to tabular ML presents unique challenges due t
Externí odkaz:
http://arxiv.org/abs/2409.12642
Autor:
Stoian, Mihail, Kipf, Andreas
We revisit the join ordering problem in query optimization. The standard exact algorithm, DPccp, has a worst-case running time of $O(3^n)$. This is prohibitively expensive for large queries, which are not that uncommon anymore. We develop a new algor
Externí odkaz:
http://arxiv.org/abs/2409.08013
Autor:
Stoian, Mihail
The dynamic programming solution to the traveling salesman problem due to Bellman, and independently Held and Karp, runs in time $O(2^n n^2)$, with no improvement in the last sixty years. We break this barrier for the first time by designing an algor
Externí odkaz:
http://arxiv.org/abs/2405.03018
Autor:
Stoian, Mihail
In their seminal work on subset convolution, Bj\"orklund, Husfeldt, Kaski and Koivisto introduced the now well-known $O(2^n n^2)$-time evaluation of the subset convolution in the sum-product ring. This sparked a wave of remarkable results for fundame
Externí odkaz:
http://arxiv.org/abs/2404.18522
Autor:
Stoian, Mihail
Exponential-time approximation has recently gained attention as a practical way to deal with the bitter NP-hardness of well-known optimization problems. We study for the first time the $(1 + \varepsilon)$-approximate min-sum subset convolution. This
Externí odkaz:
http://arxiv.org/abs/2404.11364
Column encoding schemes have witnessed a spark of interest with the rise of open storage formats (like Parquet) in data lakes in modern cloud deployments. This is not surprising -- as data volume increases, it becomes more and more important to reduc
Externí odkaz:
http://arxiv.org/abs/2403.17229
Amplification by subsampling is one of the main primitives in machine learning with differential privacy (DP): Training a model on random batches instead of complete datasets results in stronger privacy. This is traditionally formalized via mechanism
Externí odkaz:
http://arxiv.org/abs/2403.04867
Deep learning models have shown their strengths in various application domains, however, they often struggle to meet safety requirements for their outputs. In this paper, we introduce PiShield, the first package ever allowing for the integration of t
Externí odkaz:
http://arxiv.org/abs/2402.18285