1,721,073 research outputs found
High performance: closed frequent itemsets mining inspired by emerging computer architectures
No full textReport on the 1st Workshop on Reaching Efficiency in Neural Information Retrieval (ReNeuIR 2022) at SIGIR 2022
No full textAs Information Retrieval (IR) researchers, we not only develop algorithmic solutions to hard problems, but we also insist on a proper, multifaceted evaluation of ideas. The IR literature on the fundamental topic of retrieval and ranking, for instance, has a rich history of studying the effectiveness of indexes, retrieval algorithms, and complex machine learning rankers and, at the same time, quantifying their computational costs, from creation and training to application and inference. This is evidenced, for example, by more than a decade of research on efficient training and inference of large decision forest models in Learning to Rank (LtR). As we move towards even more complex, deep learning models in a wide range of applications, questions on efficiency have once again become relevant with renewed urgency. Indeed, efficiency is no longer limited to time- and space-efficiency; instead it has found new, challenging dimensions that stretch to resource-, sample- and energy-efficiency with ramifications for researchers, users, and the environment.
As a step towards bringing together experts from industry and academia and creating a forum for a critical discussion and the promotion of efficiency in the era of Neural Information Retrieval (NIR), we held the ReNeuIR workshop on July 15th, 2022 as a hybrid event---in person in Madrid, Spain along with online attendees---in conjunction with ACM SIGIR 2022. Recognizing the importance of this topic, approximately 80 participants answered our call and attended the workshop over three sessions. The event included a total of two keynotes and eight paper presentations, and concluded with a lively discussion where participants helped identify gaps in existing research and brainstormed future research directions. We had consensus in recognizing that efficiency is not simply latency, that a holistic, concrete definition of efficiency is needed to guide researchers and reviewers alike, and that more research is necessary in the development of efficiency-centered evaluation metrics and standard benchmark datasets, platforms, and tools
Social–Spatiotemporal Analysis of Topical and Polarized Communities in Online Social Networks
No full textEfficiency/Effectiveness Trade-offs in Learning to Rank
No full textIn the last years, Learning to Rank (LtR) had a significant influence on several tasks in the Information Retrieval field, with large research efforts coming both from the academia and the industry. Indeed, efficiency requirements must be fulfilled in order to make an effective research product deployable within an industrial environment. The evaluation of a model can be too expensive due to its size, the features used and several other factors. This tutorial discusses the recent solutions that allow to build an effective ranking model that satisfies temporal budget constrains at evaluation time
LSDS-IR'11: The 9th Workshop on Large-scale and Distributed Systems for Information Retrieval
No full textThe growth of the Web and user bases lead to important performance problems for large-scale Web search engines. The LSDS- IR '11 workshop focuses on research contributions related to the scalability and efficiency of distributed information retrieval (IR) systems. The workshop also encourages contributions that propose different ways of leveraging diversity and multiplicity of resources available in distributed systems. More specifically, we are interested in novel applications, models, and architectures that deal with efficiency and scalability of distributed IR systems. © 2011 Authors
DCI_Closed: A Fast and Memory Efficient Algorithm to Mine Frequent Closed Itemsets
No full textPublicazione elettronica CEUR: http://ceur-ws.or
A Unifying Framework for Mining Approximate Top-k Binary Patterns
No full textA major mining task for binary matrixes is the extraction of approximate top-k patterns that are able to concisely describe the input data. The top-k pattern discovery problem is commonly stated as an optimization one, where the goal is to minimize a given cost function, see the accuracy of the data description. In this work, we review several greedy algorithms, and discuss PANDA(+), an algorithmic framework able to optimize different cost functions generalized into a unifying formulation. We evaluated the goodness of the algorithm by measuring the quality of the extracted patterns. We adapted standard quality measures to assess the capability of the algorithm to discover both the items and transactions of the patterns embedded in the data. The evaluation was conducted on synthetic data, where patterns were artificially embedded, and on real-world text collection, where each document is labeled with a topic. Finally, in order to qualitatively evaluate the usefulness of the discovered patterns, we exploited PANDA(+) to detect overlapping communities in a bipartite network. The results show that PANDA(+) is able to discover high-quality patterns in both synthetic and real-world datasets
Feature partitioning for robust tree ensembles and their certification in adversarial scenarios
Get PDFMachine learning algorithms, however effective, are known to be vulnerable in adversarial scenarios where a malicious user may inject manipulated instances. In this work, we focus on evasion attacks, where a model is trained in a safe environment and exposed to attacks at inference time. The attacker aims at finding a perturbation of an instance that changes the model outcome.We propose a model-agnostic strategy that builds a robust ensemble by training its basic models on feature-based partitions of the given dataset. Our algorithm guarantees that the majority of the models in the ensemble cannot be affected by the attacker. We apply the proposed strategy to decision tree ensembles, and we also propose an approximate certification method for tree ensembles that efficiently provides a lower bound of the accuracy of a forest in the presence of attacks on a given dataset avoiding the costly computation of evasion attacks.Experimental evaluation on publicly available datasets shows that the proposed feature partitioning strategy provides a significant accuracy improvement with respect to competitor algorithms and that the proposed certification method allows ones to accurately estimate the effectiveness of a classifier where the brute-force approach would be unfeasible
Right-protected data publishing with provable distance-based mining
No full textProtection of one's intellectual property is a topic with important technological and legal facets. We provide mechanisms for establishing the ownership of a dataset consisting of multiple objects. The algorithms also preserve important properties of the dataset, which are important for mining operations, and so guarantee both right protection and utility preservation. We consider a right-protection scheme based on watermarking. Watermarking may distort the original distance graph. Our watermarking methodology preserves important distance relationships, such as: the Nearest Neighbors (NN) of each object and the Minimum Spanning Tree (MST) of the original dataset. This leads to preservation of any mining operation that depends on the ordering of distances between objects, such as NN-search and classification, as well as many visualization techniques. We prove fundamental lower and upper bounds on the distance between objects post-watermarking. In particular, we establish a restricted isometry property, i.e., tight bounds on the contraction/expansion of the original distances. We use this analysis to design fast algorithms for NN-preserving and MST-preserving watermarking that drastically prune the vast search space. We observe two orders of magnitude speedup over the exhaustive schemes, without any sacrifice in NN or MST preservation. © 1989-2012 IEEE
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