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检索条件"任意字段=Conference on Space-Efficient Data Structures, Streams, and Algorithms"

共 740 条记录，以下是11-20 订阅

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efficient Off-Policy algorithms for Structured Markov Decision Processes 62

Efficient Off-Policy Algorithms for Structured Markov Decisi...

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62nd IEEE conference on Decision and Control (CDC)

作者： Ganguly, Sourav Diddigi, Raghuram Bharadwaj Prabuchandran, K. J. Indian Inst Technol Dept Comp Sci & Engn Dharwad Karnataka India Int Inst Informat Technol Bangalore Karnataka India

ISBN: (纸本)9798350301243

Reinforcement Learning (RL) algorithms help in training an autonomous agent to learn the optimal actions in an unknown environment. RL, in conjunction with neural networks, known as deep RL, has achieved remarkable success in many real-life practical applications. However, most of these algorithms are complex to train and require a lot of data to learn optimal decisions. Hence, it is imperative to develop RL algorithms that are simple and data efficient. In this work, we propose off-policy RL algorithms that can exploit special structures present in the optimal policy of the underlying Markov Decision process. The off-policy learning enables us to make use of the available data efficiently. To this end, we first propose an offpolicy algorithm that estimates the value function of only those policies with the optimal policy structure to determine the best policy. We then propose two novel off-policy algorithms utilizing Upper Confidence Bound (UCB) with less time and space complexity than our first algorithm. Through extensive experimental evaluations on RL benchmark tasks, we illustrate the efficacy of the proposed algorithms.

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A Tale of Two efficient Value Iteration algorithms for Solving Linear MDPs with Large Action space 26

A Tale of Two Efficient Value Iteration Algorithms for Solvi...

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26th International conference on Artificial Intelligence and Statistics (AISTATS)

作者： Xu, Zhaozhuo Song, Zhao Shrivastava, Anshumali Rice Univ Houston TX 77251 USA Adobe Res San Francisco CA USA

Markov Decision Process (MDP) with large action space naturally occurs in many applications such as language processing, information retrieval, and recommendation system. There have been various approaches to solve these MDPs through value iteration (VI). Unfortunately, all VI algorithms require expensive linear scans over the entire action space for value function estimation during each iteration. To this end, we present two provable Least-Squares Value Iteration (LSVI) algorithms with runtime complexity sublinear in the number of actions for linear MDPs. We formulate the value function estimation procedure in VI as an approximate maximum inner product search problem and propose a Locality Sensitive Hashing (LSH) type data structure to solve this problem with sublinear time complexity. Our major contribution is combining the guarantees of approximate maximum inner product search with the regret analysis of reinforcement learning. We prove that, with the appropriate choice of approximation factor, there exists a sweet spot. Our proposed Sublinear LSVI algorithms maintain the same regret as the original LSVI algorithms while reducing the runtime complexity to sublinear in the number of actions. To the best of our knowledge, this is the first work that combines LSH with reinforcement learning that results in provable improvements. We hope that our novel way of combining data structures and the iterative algorithm will open the door for further study into the cost reduction in reinforcement learning.

关键词： Reinforcement learning

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Parallel Minimum -Weight Parity Factor Decoding for Quantum Error Correction 5

Parallel Minimum -Weight Parity Factor Decoding for Quantum ...

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2024 International conference on Quantum Computing and Engineering

作者： Yang, Liu Wu, Yue Zhong, Lin Yale Univ Dept Comp Sci POB 2158 New Haven CT 06520 USA

ISBN: (纸本)9798331541378

The Minimum -Weight Parity Factor (MWPF) decoder is renowned for its accuracy in Quantum Error Correction (QEC) for various space-time codes, including surface codes and quantum LDPC codes. This work explores the scalability of MWPF decoders through parallelization, similar to parallel MWPM decoders. We introduce a dynamic fusion graph to schedule fusion operations in dynamic quantum circuits. It allows parts of the decoding graph to be solved and fused at a later stage, without having the full fusion tree at the very beginning. It also reduces the overhead caused by the traversing of the fusion tree. The dynamic fusion graph represents a novel and efficient method for real-time QEC decoding, providing enhancements in throughput and scalability for fault -tolerant quantum computing.

关键词： and Cluster Computing data structures and algorithms Parallel Quantum Error Correction Distributed

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Leveraging Task-Specific VAEs for efficient Exemplar Generation in HAR 7th

Leveraging Task-Specific VAEs for Efficient Exemplar Generat...

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7th IFIP WG 5.5 International Cross-Domain conference on Internet of Things

作者： Kann, Bonpagna Castellanos-Paez, Sandra Rombourg, Romain Lalanda, Philippe Univ Grenoble Alpes CNRS Grenoble INP LIG F-38000 Grenoble France Cambodia Acad Digital Technol Inst Digital Res & Innovat Phnom Penh 121002 Cambodia Univ Grenoble Alpes CNRS Grenoble INP G2Elab F-38000 Grenoble France

ISBN: (纸本)9783031819025;9783031819001;9783031818998

The emerging technologies of smartphones and wearable devices have transformed Human Activity Recognition (HAR), offering a rich source of sensor data for building an automated system to recognize people's daily activities. The sensor-based HAR data also enables Machine Learning (ML) algorithms to classify various activities, indicating a new era of intelligent systems for health monitoring and diagnostics. However, integrating ML into these systems faces the challenge of catastrophic forgetting, where models lose proficiency in previously learned activities when introduced to new ones by users. Continual Learning (CL) has emerged as a solution, enabling models to learn continuously from evolving data streams while reducing forgetting of past knowledge. Within CL methodologies, the use of generative models, such as Variational Autoencoders (VAEs), for example, has drawn significant interest for their capacity to generate synthetic data. This reduces storage demands by creating on-demand samples. However, the application of VAEs with a CL classifier has been limited to low-dimensional data or fine-grained features, leaving a gap in harnessing raw, high-dimensional sensor data for the HAR model. Our research aims to bridge this gap by constructing VAEs with a filtering mechanism for direct training with raw sensor data from the HAR dataset, enhancing CL models' capability in class-incremental learning scenario. We demonstrate that VAE with a boundary box sampling and filtering process significantly outperforms both traditional and hybrid exemplar CL methods, offering a more balanced and diverse training set that enhances the knowledge acquisition of the model. Our findings also emphasize the importance of sampling strategies in the latent space of VAEs to maximize data diversity, crucial for recognizing the variability in human activities for better representation of each activity in each CL task.

关键词： Continual Learning HAR Replay Methods VAE

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Scube: efficient Summarization for Skewed Graph streams 42

Scube: Efficient Summarization for Skewed Graph Streams

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42nd IEEE International conference on Distributed Computing Systems (ICDCS)

作者： Chen, Ming Zhou, Renxiang Chen, Hanhua Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Cluster & Grid Comp Lab Serv Comp Technol & Syst LabSch Comp Sci & Techn Wuhan Peoples R China

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Graph stream, which represents an evolving graph updating as an infinite edge stream, is a special emerging graph data model widely adopted in big data analysis applications. Entirely storing the continuously produced and tremendously large-scale datasets is impractical. Therefore, graph stream summarization structures which support approximate graph stream storage and management attract much recent attention. Existing designs commonly leverage a compressive matrix and use hash-based schemes to map each edge to a bucket of the matrix. Accordingly, they store the edges associated with the same node in the same row or column of the matrix. We show that existing designs suffer from unacceptable query latency and precision in the presence of node degree skewness in graph streams. We argue that the key to efficient graph stream summarization is to identify the high-degree nodes and leverage a differentiated strategy for the associated edges. However, it is not trivial to estimate the degree of a node in real-time graph streams due to the rigorous requirements ()I' space and time efficiency. Moreover, the existence of duplicate edges makes high-degree nodes identification difficult. To solve the problem, we propose Scube, an efficient summarization structure for skewed graph streams. Two factors contribute to the efficiency of Scube. First, Scube proposes a space and computation efficient probabilistic counting scheme to identify high-degree nodes in a graph stream. Second, Scube differentiates the storage strategy for the edges associated with high-degree nodes by dynamically allocating multiple rows or columns. We conduct comprehensive experiments to evaluate the performance of Scube on large-scale real-world datasets. The results show that Scube significantly reduces the query latency over a graph stream by 48%-99%, as well as achieving acceptable query accuracy compared to the state-of-the-art designs.

关键词： Graph stream summarization skewness

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The LSM Design space and its Read Optimizations 39

The LSM Design Space and its Read Optimizations

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39th IEEE International conference on data Engineering, ICDE 2023

作者： Sarkar, Subhadeep Dayan, Niv Athanassoulis, Manos Boston University BostonMA United States University of Toronto TorontoON Canada

ISBN: (纸本)9798350322279

Log-structured merge (LSM) trees have emerged as one of the most commonly used storage-based data structures in modern data systems as they offer high throughput for writes and good utilization of storage space. However, LSM-trees were not originally designed to facilitate efficient reads. Thus, state-of-the-art LSM engines employ numerous optimization techniques to make reads efficient. The goal of this tutorial is to present the fundamental principles of the LSM paradigm along with the various optimization techniques and hybrid designs adopted by LSM engines to accelerate *** this, we first discuss the basic LSM operations and their access patterns. We then discuss techniques and designs that optimize point and range lookups in LSM-trees: (i) index and (ii) filter data structures, (iii) caching, and (iv) read-friendly data layouts. Next, we present the performance tradeoff between writes and reads, outlining the rich design space of the LSM paradigm and how one can navigate it to improve query performance. We conclude by discussing practical problems and open research challenges. This will be a 1.5-hour tutorial. © 2023 IEEE.

关键词： data structures

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Fast Dynamic Sampling for Determinantal Point Processes 27

Fast Dynamic Sampling for Determinantal Point Processes

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27th International conference on Artificial Intelligence and Statistics (AISTATS)

作者： Song, Zhao Yin, Junze Zhang, Lichen Zhang, Ruizhe Adobe Res Double Adobe AZ 85617 USA Boston Univ Boston MA 02215 USA MIT Cambridge MA 02139 USA Univ Calif Berkeley Berkeley CA 94720 USA

In this work, we provide fast dynamic algorithms for repeatedly sampling from distributions characterized by Determinantal Point Processes (DPPs) and Nonsymmetric Determinantal Point Processes (NDPPs). DPPs are a very well-studied class of distributions on subsets of items drawn from a ground set of cardinality n characterized by a symmetric n x n kernel matrix L such that the probability of any subset is proportional to the determinant of its corresponding principal submatrix. Recent work has shown that the kernel symmetry constraint can be relaxed, leading to NDPPs, which can better model data in several machine learning applications. Given a low-rank kernel matrix L = L+ L-inverted perpendicular is an element of R-n x n and its corresponding eigendecomposition specified by {lambda(i), u(i)} (d)(i=1) where d <= n is the rank, we design a data structure that uses O(nd) space and preprocesses data in O(nd(omega - 1)) time where omega approximate to 2.37 is the exponent of matrix multiplication. The data structure can generate a sample according to DPP distribution in time O(|E|(3) log n + |E|(omega - 1) d(2)) or according to NDPP distribution in time O((|E|(3) log n + |E|(omega - 1)d(2)) (1 + w)(d)) for E being the sampled indices and w is a data-dependent parameter. This improves upon the space and preprocessing time over prior works, and achieves a state-of-the-art sampling time when the sampling set is relatively dense. At the heart of our data structure is an efficient sampling tree that can leverage batch initialization and fast inner product query simultaneously.

关键词： data structures

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29th International Computing and Combinatorics conference, COCOON 2023

29th International Computing and Combinatorics Conference, C...

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29th International Computing and Combinatorics conference, COCOON 2023

ISBN: (纸本)9783031491924

The proceedings contain 60 papers. The special focus in this conference is on Computing and Combinatorics. The topics include: Sink Location Problems in Dynamic Flow Grid Networks;list 3-Coloring on Comb-Convex and Caterpillar-Convex Bipartite Graphs;parameterized algorithms for Cluster Vertex Deletion on Degree-4 Graphs and General Graphs;sum-of-Local-Effects data structures for Separable Graphs;variants of Euclidean k-Center Clusterings;red-Black Spanners for Mixed-Charging Vehicular Networks;self-stabilizing (Δ+ 1 ) -Coloring in Sublinear (in Δ ) Rounds via Locally-Iterative algorithms;on Detecting Some Defective Items in Group Testing;an efficient data Analysis Method for Big data Using Multiple-Model Linear Regression;conditional Automatic Complexity and Its Metrics;multi-Load Agent Path Finding for Online Pickup and Delivery Problem;improved Sourcewise Roundtrip Spanners with Constant Stretch;randomized data Partitioning with efficient Search, Retrieval and Privacy-Preservation;the k Edge-Vertex Domination Problem;resource-Adaptive Newton’s Method for Distributed Learning;DR-Submodular Function Maximization with Adaptive Stepsize;on the Routing Problems in Graphs with Ordered Forbidden Transitions;delaying Decisions and Reservation Costs;A PTAS Framework for Clustering Problems in Doubling Metrics;A Physical Zero-Knowledge Proof for Sumplete, a Puzzle Generated by ChatGPT;streaming and Query Once space Complexity of Longest Increasing Subsequence;approximating Decision Trees with Priority Hypotheses;approximating the λ -low-density Value;Exponential Time Complexity of the Complex Weighted Boolean #CSP;hardness and Approximation for the Star β -Hub Routing Cost Problem in Δβ -Metric Graphs;Linear Time algorithms for NP-Hard Problems Restricted to GaTEx Graphs;polynomial Turing Compressions for Some Graph Problems Parameterized by Modular-Width.

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29th International Computing and Combinatorics conference, COCOON 2023

29th International Computing and Combinatorics Conference, C...

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29th International Computing and Combinatorics conference, COCOON 2023

ISBN: (纸本)9783031491894

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Comparison of supervised algorithms for automated data analysis in existing buildings

Comparison of supervised algorithms for automated data analy...

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2023 International conference on the Built Environment in Transition, CISBAT 2023

作者： Stinner, Florian Kümpel, Alexander Müller, Dirk RWTH Aachen University E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate Mathieustraße 10 Aachen52074 Germany

Non-residential buildings are equipped with building automation systems (BAS). BAS provide a vast amount of building operation data. However, the analysis of this data is laborious and requires a scalable and automated process. The authors developed the BUDO schema for a standardized, object-oriented description of monitoring metadata. The translation process employs artificial intelligence to translate the properties of data streams (labels, time series) from BAS into the BUDO schema. The translated data is then analyzed using various applications to detect inefficiencies, such as incorrect placement of outdoor temperature sensors or a weekend shutdown that has not been properly implemented. The paper presents a process that automatically translates, structures and analyzes operational data to identify potential for energy savings. The OOM4ABDO project monitors 138 buildings to analyze and improve building operations, whose subset of data we are using here. In addition, we use an open source data set of about 70 buildings. We compare several feature extraction methods and classifiers on their ability to support each of the stages (label translation, time series translation, application). The algorithms used reach F1 scores of up to 98% for label translation, 90% for time series translation and 92% for applications. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Time series

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