Open Access
Space-Efficient Data Structures, Streams, and Algorithms
Joan Boyar,Faith Ellen +1 more
- 01 Jan 2013
28
TL;DR: It is proved matching upper and lower bounds for the deterministic and randomized query complexity of Θ(n log n) and Θ (n log log n), respectively.
read more
Abstract: We study the query complexity of determining a hidden permutation. More specifically, we study the problem of learning a secret (z, π) consisting of a binary string z of length n and a permutation π of [n]. The secret must be unveiled by asking queries x ∈ {0, 1}n, and for each query asked, we are returned the score fz,π(x) defined as fz,π(x) := max{i ∈ [0..n] | ∀j ≤ i : zπ(j) = xπ(j)} ; i.e., the length of the longest common prefix of x and z with respect to π. The goal is to minimize the number of queries asked. We prove matching upper and lower bounds for the deterministic and randomized query complexity of Θ(n log n) and Θ(n log log n), respectively.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
PerfAugur: Robust diagnostics for performance anomalies in cloud services
Sudip Roy,Arnd Christian König,Igor Dvorkin,Manish Kumar +3 more
- 13 Apr 2015
TL;DR: PerfAugur, an automated system for mining service logs to identify anomalies and help formulate data-driven hypotheses, includes a suite of efficient mining algorithms for detecting significant anomalies in system behavior, along with potential explanations for such anomalies, without the need for an explicit supervision signal.
Space-Efficient Frameworks for Top-k String Retrieval
TL;DR: This work presents the first linear-space framework that is capable of handling arbitrary score functions with near-optimal O(p + klog k) query time and derives compact space and succinct space indexes (for some specific score functions).
45
Towards a final analysis of pairing heaps
Seth Pettie
- 01 Jan 2005
TL;DR: In this article, it was shown that pairing heaps can support all priority queue operations in logarithmic time and is known to be extremely efficient in practice, even for splay trees.
45
A new quantile tracking algorithm using a generalized exponentially weighted average of observations
TL;DR: This work presents a lightweight quantile estimator using a generalized form of the Exponentially Weighted Average that outperforms legacy state-of-the-art quantile tracking estimators and achieves faster adaptivity in dynamic environments.
Smooth heaps and a dual view of self-adjusting data structures
László Kozma,Thatchaphol Saranurak +1 more
- 20 Jun 2018
TL;DR: The smooth heap is shown to be the heap-counterpart of Greedy, the BST algorithm with the strongest proven and conjectured properties from the literature, conjectured to be instance-optimal, initiating a theory of dynamic optimality for heaps.
19
References
Reinforcement learning: a survey
TL;DR: Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Differential privacy
Cynthia Dwork
- 10 Jul 2006
TL;DR: In this article, the authors give a general impossibility result showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved, and suggest a new measure, differential privacy, which, intuitively, captures the increased risk to one's privacy incurred by participating in a database.
•Posted Content
Reinforcement Learning: A Survey
TL;DR: A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
5.9K
Approximate nearest neighbors: towards removing the curse of dimensionality
Piotr Indyk,Rajeev Motwani +1 more
- 23 May 1998
TL;DR: In this paper, the authors present two algorithms for the approximate nearest neighbor problem in high-dimensional spaces, for data sets of size n living in R d, which require space that is only polynomial in n and d.
•Book
Approximation Algorithms
Vijay V. Vazirani
- 02 Jul 2001
TL;DR: Covering the basic techniques used in the latest research work, the author consolidates progress made so far, including some very recent and promising results, and conveys the beauty and excitement of work in the field.
4.5K
Related Papers (5)
Weifeng Sun,Yudan Zhu,Zhiyuan Su,Dong Jiao,Mingchu Li +4 more
- 18 Dec 2010
Said Elnaffar,T. Katayama,Ho Tu Bao +2 more
- 01 Nov 2006