Zobrazeno 1 - 10
of 8 981
pro vyhledávání: '"A. Stoian"'
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
Autor:
Stoian, Mihail
We study the fundamental scheduling problem $1\|\sum p_jU_j$. Given a set of $n$ jobs with processing times $p_j$ and deadlines $d_j$, the problem is to select a subset of jobs such that the total processing time is maximized without violating the de
Externí odkaz:
http://arxiv.org/abs/2402.16847
Deep learning has been at the core of the autonomous driving field development, due to the neural networks' success in finding patterns in raw data and turning them into accurate predictions. Moreover, recent neuro-symbolic works have shown that inco
Externí odkaz:
http://arxiv.org/abs/2402.11362