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Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Existing pairwise learning algorithms do not perform well in the generality, scalability and efficiency simultaneously. ...
To address these challenging problems, in this paper, we first analyze the relationship between the statistical accuracy and the regularized empire risk for pairwise loss. ...
The scalability and efficiency are still the bottlenecks of existing pairwise learning algorithms. ...
doi:10.1609/aaai.v33i01.33013697
fatcat:c3zaqqwmwjgr5dx4gfqu7z6pp4
WMRB: Learning to Rank in a Scalable Batch Training Approach
[article]
2017
arXiv
pre-print
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We propose a new learning to rank algorithm, named Weighted Margin-Rank Batch loss (WMRB), to extend the popular Weighted Approximate-Rank Pairwise loss (WARP). ...
WMRB uses a new rank estimator and an efficient batch training algorithm. ...
However, it is not scalable to large item set in practice due to its intrinsic online learning fashion. ...
arXiv:1711.04015v1
fatcat:jrmayomu6vetbi5pfuvmc73ccy
On Training Knowledge Graph Embedding Models
2021
Information
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., RDF graphs) in an efficient and scalable manner. The key to success of these models is their ability to learn low-rank vector representations for knowledge graph entities and relations. ...
We finally investigate the effects of specific choices on the scalability and accuracy of knowledge graph embedding models. ...
This approach provides scalable and efficient embeddings learning as it has linear time and space complexity. ...
doi:10.3390/info12040147
fatcat:dsavhnvr5zcuflnu3c5dp5ap5y
SimpleNPKL
2009
Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09
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In contrast to the previous approaches, our empirical results show that our new technique achieves the same accuracy, but is significantly more efficient and scalable. ...
In this paper, we propose an efficient approach to NPK learning from side information, referred to as SimpleNPKL, which can efficiently learn non-parametric kernels from large sets of pairwise constraints ...
Acknowledgments This research was in part supported by Singapore MOE AcRF Tier-1 Research Grant (RG15/08) and Research Grant (RG67/07). ...
doi:10.1145/1553374.1553537
dblp:conf/icml/ZhuangTH09
fatcat:2g7sdd7qwfgldhcpcnyb5r6al4
Private Two-Party Cluster Analysis Made Formal Scalable
[article]
2019
arXiv
pre-print
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Crucially, our solution employs modular design and judicious use of cryptography to achieve high degrees of efficiency and extensibility. ...
For example, end-to-end execution of our secure approximate protocol, over 1M 10-dimensional records, completes in 35 sec, transferring only 896KB and achieving 97.09% accuracy. ...
We combine our protocols with efficient approximate clustering in order to achieve the best of both worlds: strong security guarantees and scalability. ...
arXiv:1904.04475v2
fatcat:mss4mujjgngbheypvv6rurb7im
Scalable Multi-grained Cross-modal Similarity Query with Interpretability
2021
Data Science and Engineering
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queries. (4) A distributed query algorithm is proposed to improve the scalability of our framework. ...
Existing researches generally focus on query accuracy by designing complex deep neural network models and hardly consider query efficiency and interpretability simultaneously, which are vital properties ...
Acknowledgements We would like to thank selfless friends and professional reviewers for all the insightful advices. ...
doi:10.1007/s41019-021-00162-4
fatcat:7tdgbtoq2jc45ixrdltrl4nofu
TOD: GPU-accelerated Outlier Detection via Tensor Operations
[article]
2022
arXiv
pre-print
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We propose TOD, a system for efficient and scalable outlier detection (OD) on distributed multi-GPU machines. ...
This decomposition enables TOD to accelerate OD computations by leveraging recent advances in deep learning infrastructure in both hardware and software. ...
Zhihao Jia is partially supported by an National Science Foundation award CNS-2147909, and a Tang Endowment. ...
arXiv:2110.14007v2
fatcat:5fwqcku3z5ettdus3hlwu5wft4
Scalable Learning of Non-Decomposable Objectives
[article]
2017
arXiv
pre-print
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Modern retrieval systems are often driven by an underlying machine learning model. The goal of such systems is to identify and possibly rank the few most relevant items for a given query or context. ...
In practice, due to the scalability limitations of existing approaches for optimizing such objectives, large-scale retrieval systems are instead trained to maximize classification accuracy, in the hope ...
Finally, and most importantly, our bounds give rise to an optimization approach for non-decomposable learning metrics that is highly scalable and that is applicable to truly large datasets. ...
arXiv:1608.04802v2
fatcat:v5b2le5e3bbzxk3fqavqncxipe
On the Accuracy and Scalability of Probabilistic Data Linkage Over the Brazilian 114 Million Cohort
2018
IEEE journal of biomedical and health informatics
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In this paper, we present AtyImo, a hybrid probabilistic linkage tool optimized for high accuracy and scalability in massive data sets. ...
In terms of scalability, we present AtyImo's ability to link the entire cohort in less than nine days using Spark and scaling up to 20 million records in less than 12s over heterogeneous (CPU+GPU) architectures ...
We discuss and evaluate accuracy, scalability and performance results achieved in experimental and real scenarios. ...
doi:10.1109/jbhi.2018.2796941
pmid:29505402
pmcid:PMC7198121
fatcat:cuvg5hzwqvgfpnlz77on64asnq
Learning Sparse Log-Ratios for High-Throughput Sequencing Data
[article]
2021
bioRxiv
pre-print
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However, the space of these log-ratios grows combinatorially with the dimension of the input, and as a result, existing learning algorithms do not scale to increasingly common high-dimensional datasets ...
As well as dramatically reducing runtime, our method outperforms its competitors in terms of sparsity and predictive accuracy, as measured across a wide range of benchmark datasets. ...
SCALABILITY INTERPRETABILITY SPARSITY ACCURACY CODACORE (OURS) + + + + PAIRWISE LOG-RATIOS (GREENACRE, 2019B) − + + − SELBAL (RIVERA-PINTO ET AL., 2018)
Table 2 . 2 Evaluation metrics shown for each ...
doi:10.1101/2021.02.11.430695
fatcat:hepgni7uabbpnl6so32j5hybfq
GRAIL
2019
Proceedings of the VLDB Endowment
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To address this major drawback, we present GRAIL, a generic framework to learn compact time-series representations that preserve the properties of a user-specified comparison function. ...
The effectiveness and the scalability of time-series mining techniques critically depend on design choices for three components responsible for (i) representing; (ii) comparing; and (iii) indexing time ...
We also thank Christos Faloutsos and Eamonn Keogh for useful discussions and Luis Gravano and Daniel Hsu for invaluable feedback. ...
doi:10.14778/3342263.3342648
fatcat:m7gtrlakgzb4peibo2wvtszvq4
Detection of stealthy malware activities with traffic causality and scalable triggering relation discovery
2014
Proceedings of the 9th ACM symposium on Information, computer and communications security - ASIA CCS '14
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We use these triggering relations to reason the occurrences of network events and to pinpoint stealthy malware activities. We define a new problem of triggering relation discovery of network events. ...
Our solution is based on domain-knowledge guided advanced learning algorithms. ...
Our approach utilizes probabilistic machine learning algorithms and achieves high scalability and detection accuracy. We introduce a scalable feature extraction method referred to as Pairing. ...
doi:10.1145/2590296.2590309
dblp:conf/ccs/ZhangYR14
fatcat:kffyvrimzrca5laimnsovbcbv4
PQ-WGLOH: A bit-rate scalable local feature descriptor
2012
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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The descriptor achieves about 95% bits reduction compared with 128-Byte SIFT and allows adaptation of descriptor lengths to support user required performance. ...
We achieve competing matching and retrieval performance with SIFT, GLOH with much fewer bits. ...
The overlap ratio for PQ-WGLOH is 0.8604, which is slightly worse than SIFT and GLOH, with 0.8902 and 0.8729 respectively. CHoG achieves localization accuracy of 0.8134. ...
doi:10.1109/icassp.2012.6288040
dblp:conf/icassp/WangDWG12
fatcat:6eiylmgobjhpbckkuqltuprhpy
Robust continuous clustering
2017
Proceedings of the National Academy of Sciences of the United States of America
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We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. ...
Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank. clustering | data analysis | unsupervised learning ...
that can untangle mixed clusters, and optimization is performed by efficient and scalable numerical methods. ...
doi:10.1073/pnas.1700770114
pmid:28851838
pmcid:PMC5603997
fatcat:ytm2vunu4nfl3cx3tblsqgifdy
Spatio-temporal patterns in network events
2010
Proceedings of the 6th International COnference on - Co-NEXT '10
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Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. ...
The first author and the last author are partially supported by grants from NSF CyberTrust program, NSF NetSE program, an IBM SUR grant, and a grant from Intel research council. ...
On both synthetic and real event datasets, Tar achieves an accuracy above 0.75, under missing 40% percent of symptom events. ...
doi:10.1145/1921168.1921172
dblp:conf/conext/WangSAL10
fatcat:mruamubuebb3hbfp3s3ocrmunq
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