1,721,046 research outputs found
Diameter and k-Center in Sliding Windows
Get PDFIn this paper we develop streaming algorithms for the diameter problem and the k-center clustering problem in the sliding window model. In this model we are interested in maintaining a solution for the N most recent points of the stream. In the diameter problem we would like to maintain two points whose distance approximates the diameter of the point set in the window. Our algorithm computes a (3 + epsilon)-approximation and uses O(1/epsilon*ln(alpha)) memory cells, where alpha is the ratio of the largest and smallest distance and is assumed to be known in advance. We also prove that under reasonable assumptions obtaining a (3 - epsilon)-approximation requires Omega(N1/3) space.
For the k-center problem, where the goal is to find k centers that minimize the maximum distance of a point to its nearest center, we obtain a (6 + epsilon)-approximation using O(k/epsilon*ln(alpha)) memory cells and a (4 + epsilon)-approximation for the special case k = 2. We also prove that any algorithm for the 2-center problem that achieves an approximation ratio of less than 4 requires Omega(N^{1/3}) space
L'impact disparate du bruit sur les algorithmes d'apprentissage quantique
No full textL'informatique quantique, l'un des voyages scientifiques les plus passionnants de notre époque, offre un potentiel remarquable en promettant de résoudre rapidement des problèmes computationnels. Cependant, la mise en œuvre pratique de ces algorithmes représente un défi immense, avec un ordinateur quantique universel et tolérant aux erreurs restant insaisissable. Actuellement, des dispositifs quantiques à court terme émergent, mais ils sont confrontés à des limitations majeures, notamment un niveau élevé de bruit et une capacité d'intrication limitée. L'efficacité pratique de ces dispositifs, en particulier en raison du bruit quantique, suscite des débats. Motivée par cette situation, cette thèse explore l'impact profond du bruit sur les algorithmes d'apprentissage quantique en trois dimensions clés. Tout d'abord, elle examine l'influence du bruit sur les algorithmes quantiques variationnels, en particulier les méthodes quantiques "à noyaux". Nos résultats révèlent des disparités marquées entre le bruit unital et non-unital, remettant en question les conclusions antérieures sur ces algorithmes bruyants. Ensuite, elle aborde l'apprentissage des dynamiques quantiques avec des mesures binaires bruyantes de l'état de Choi-Jamiolkowski, en utilisant des requêtes statistiques quantiques. L'algorithme Goldreich-Levin peut être implémenté ainsi, et nous prouvons l'efficacité de l'apprentissage dans notre modèle. Enfin, la thèse contribue à la confidentialité différentielle quantique, montrant comment le bruit quantique peut renforcer la sécurité statistique. Une nouvelle définition d'états quantiques voisins capture la structure des encodages quantiques, offrant des garanties de confidentialité plus strictes. Dans le modèle local, une équivalence est établie entre les requêtes statistiques quantiques et la confidentialité différentielle quantique locale, avec des applications à des tâches telles que le test d'hypothèse asymétrique. Les résultats sont démontrés avec l'efficacité de l'apprentissage des fonctions de parité dans ce modèle, comparé à une tâche classique exponentiellement exigeante.Quantum computing, one of the most exciting scientific journeys of our time, holds remarkable potential by promising to rapidly solve computational problems. However, the practical implementation of these algorithms poses an immense challenge, with a universal and error-tolerant quantum computer remaining an elusive goal. Currently, short-term quantum devices are emerging, but they face significant limitations, including high levels of noise and limited entanglement capacity. The practical effectiveness of these devices, particularly due to quantum noise, is a subject of debate. Motivated by this situation, this thesis explores the profound impact of noise on quantum learning algorithms in three key dimensions. Firstly, it focuses on the influence of noise on variational quantum algorithms, especially quantum kernel methods. Our results reveal significant disparities between unital and non-unital noise, challenging previous conclusions on these noisy algorithms. Next, it addresses learning quantum dynamics with noisy binary measurements of the Choi-Jamiolkowski state, using quantum statistical queries. The Goldreich-Levin algorithm can be implemented in this way, and we demonstrate the efficiency of learning in our model. Finally, the thesis contributes to quantum differential privacy, demonstrating how quantum noise can enhance statistical security. A new definition of neighboring quantum states captures the structure of quantum encodings, providing stricter privacy guarantees. In the local model, we establish an equivalence between quantum statistical queries and local quantum differential privacy, with applications to tasks like asymmetric hypothesis testing. The results are illustrated by the efficient learning of parity functions in this model, compared to a classically demanding task
Recent Progress on Correlation Clustering: From Local Algorithms to Better Approximation Algorithms and Back (Invited Talk)
Get PDFCorrelation clustering is a classic model for clustering problems arising in machine learning and data mining. Given a set of data elements represented as vertices of a graph and pairwise similarity represented as edges, the goal is to find a partition of the vertex set so as to minimize the total number of edges across the parts plus the total number of non-edges within the parts.
Introduced in the early 2000s [Bansal et al., 2004], correlation clustering has received a large amount of attention through the years. A natural linear programming relaxation was shown to have an integrality gap of at least 2 and at most 2.5 [Ailon et al., 2008] in 2005, and in 2015 at most 2.06 [Chawla et al., 2015].
In 2021, motivated by large-scale application new structural insights allowed to derive a simple, practical algorithm that achieved an O(1)-approximation in a variety of models (Massively Parallel, Sublinear, Streaming or Differentially-private) [Vincent Cohen{-}Addad et al., 2021; Cohen-Addad et al., 2022]. These new insights turned out to be a key building block in designing better algorithms: It serves as a pre-clustering of the input graph that enables algorithm with approximation guarantees significantly better than 2 [Vincent Cohen{-}Addad et al., 2023; Vincent Cohen{-}Addad et al., 2022]. It is a key component in the new algorithm that achieves a 1.44-approximation [Nairen Cao et al., 2024] and in the new local-search based 1.84-approximation for the Massively Parallel, Sublinear, and Streaming models [Vincent Cohen{-}Addad et al., 2024]. This talk will review the above recent development and what are the main open research directions.
A collection of joint works with Nairen Cao, Silvio Lattanzi, Euiwoong Lee, Shi Li, David Rasmussen Lolck, Slobodan Mitrovic, Alantha Newman, Ashkan Norouzi-Fard, Nikos Parotsidis, Marcin Pilipczuk, Jakub Tarnawski, Mikkel Thorup, Lukas Vogl, Shuyi Yan, Hanwen Zhang
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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