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Submodular Optimization over Sliding Windows
[article]
2016
arXiv
pre-print
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Specifically, we focus on the sliding window model, where we are asked to maintain a solution that considers only the last W items. ...
This algorithm matches the best known approximation guarantees for submodular optimization in insertion-only streams, a less general formulation of the problem. ...
Our Contributions In this work we give the first algorithms for monotone submodular function optimization subject to a cardinality constraint over sliding windows, prove bounds on their performance, and ...
arXiv:1610.09984v1
fatcat:b5aw7falnfenjovpiezc4urga4
Submodular Maximization over Sliding Windows
[article]
2016
arXiv
pre-print
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Different from the previous work on streaming submodular maximization, we are interested only in the recent data, and study the maximization problem over sliding windows. ...
As a consequence, we obtain the first algorithms in the sliding window model for maximizing a monotone/non-monotone submodular function under cardinality and matroid constraints. ...
However, we are not aware of any previous work dealing with streaming data over sliding windows in the context of submodular optimization. ...
arXiv:1611.00129v1
fatcat:wl7vgq3gw5b5lngnrlei2ltzpu
Efficient Representative Subset Selection over Sliding Windows
2018
IEEE Transactions on Knowledge and Data Engineering
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) over sliding windows. ...
It maintains a sequence of checkpoints and KS instances over the sliding window. Theoretically, KW is 1-ε/1+d-approximate for SMDK. ...
5.3 for SMDK over sliding windows. ...
doi:10.1109/tkde.2018.2854182
fatcat:qwlm4jfnhzakzgxrkzf5vgc2y4
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals
2015
Neural Information Processing Systems
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Theoretically, our proposed formulation provides a new understanding to the problem, and contains classic heuristic approaches such as Sliding Window+Non-Maximal Suppression (NMS) and and Efficient Subwindow ...
This paper formulates the search for a set of bounding boxes (as needed in object proposal generation) as a monotone submodular maximization problem over the space of all possible bounding boxes in an ...
Sliding Window corresponds to enumeration over Y with some level of sub-sampling (or stride), typically with a fixed aspect ratio. ...
dblp:conf/nips/SunB15
fatcat:d4rse7hxnfdsrjcdkjiy5vzhbe
Real-time influence maximization on dynamic social streams
2017
Proceedings of the VLDB Endowment
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Technically, SIM adopts the sliding window model and maintains a set of k seeds with the largest influence value over the most recent social actions. ...
The IC framework creates a checkpoint for each window slide and ensures an ε-approximate solution. ...
(Section 6)
RELATED WORK We summarize the most relevant literature from three areas: influence maximization, streaming submodular optimization and function estimation on sliding windows. ...
doi:10.14778/3067421.3067429
fatcat:yaiwmvdt6fdn5m5tj4zb6mvzfa
Submodular Optimization over Streams with Inhomogeneous Decays
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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When data is given as a stream, streaming submodular optimization (SSO) techniques are desired. ...
Existing SSO techniques can only apply to insertion-only streams where each element has an infinite lifespan, and sliding-window streams where each element has a same lifespan (i.e., window size). ...
To this end, streaming submodular optimization (SSO) techniques have been developed for insertion-only streams where a subset is selected from all historical data (Badanidiyuru et al. 2014) , and sliding-window ...
doi:10.1609/aaai.v33i01.33015861
fatcat:lgb5ha6bhfgbfmpljedhqoudry
Submodular Optimization Over Streams with Inhomogeneous Decays
[article]
2018
arXiv
pre-print
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When data is given as a stream, streaming submodular optimization (SSO) techniques are desired. ...
Existing SSO techniques can only apply to insertion-only streams where each element has an infinite lifespan, and sliding-window streams where each element has a same lifespan (i.e., window size). ...
To this end, streaming submodular optimization (SSO) techniques have been developed for insertion-only streams where a subset is selected from all historical data (Badanidiyuru et al. 2014) , and sliding-window ...
arXiv:1811.05652v1
fatcat:hyfzq466ujbmdlpskuak4h24oe
River: A Real-Time Influence Monitoring System on Social Media Streams
2018
2018 IEEE International Conference on Data Mining Workshops (ICDMW)
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the sliding window. ...
To capture the temporal information, we adopt the sliding window model [19] . Given a window of length N , a sliding window W t contains social elements whose indices are between t − N + 1 and t. ...
doi:10.1109/icdmw.2018.00203
dblp:conf/icdm/0002LWGT18
fatcat:5a7t67vsibg2jc5sp2ela6uvle
Fully Dynamic Algorithm for Constrained Submodular Optimization
[article]
2020
arXiv
pre-print
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The task of maximizing a monotone submodular function under a cardinality constraint is at the core of many machine learning and data mining applications, including data summarization, sparse regression ...
Submodular optimization over sliding windows. In Proceedings of the 26th International
Conference on World Wide Web, WWW 2017, Perth, Australia, April 3-7, 2017, pages
421-430, 2017. ...
This task is captured by the sliding window model. ...
arXiv:2006.04704v1
fatcat:enk7gqxxafg57jguhapc2svnxu
Location-aware Influence Maximization over Dynamic Social Streams
2018
ACM Transactions on Information Systems
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Technically, SIM adopts the sliding window model and maintains a seed set with the maximum influence value collectively over the most recent social actions. ...
SIC maintains a sequence of influential checkpoints over the sliding window and each checkpoint maintains a partial solution for SIM in an append-only substream of social actions. ...
RELATED WORK We summarize the literature related to this work from three areas, namely influence maximization, streaming submodular optimization, and function estimation over sliding windows. ...
doi:10.1145/3230871
fatcat:iwncw6pzq5dufilah5enpayau4
Branch and Bound Strategies for Non-maximal Suppression in Object Detection
[chapter]
2011
Lecture Notes in Computer Science
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We demonstrate that typically applied objectives have the structure of a random field model, but that the energies resulting from non-maximal suppression terms lead to the maximization of a submodular ...
This leads to a series of inter-related branch-and-bound optimizations, which we characterize by several new theoretical results. ...
The approaches cited above largely work by employing sliding windows or other window subsampling strategies, but alternatively, variants on Hough transform detections have also been used. ...
doi:10.1007/978-3-642-23094-3_28
fatcat:paiqsxjruzasdjnavc4sup6zdm
Good Graph to Optimize: Cost-Effective, Budget-Aware Bundle Adjustment in Visual SLAM
[article]
2020
arXiv
pre-print
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An efficient algorithm, called Good Graph, is developed to select size-reduced graphs optimized in local BA with condition preservation. ...
Only recent states (camera frames and map points) within the sliding window are optimized in local BA. ...
window BA [19] , and 3) visual-only SVO with sliding window BA [3] . ...
arXiv:2008.10123v1
fatcat:dlilgtwbw5b77jmfa4ppczjsam
Almost-Smooth Histograms and Sliding-Window Graph Algorithms
[article]
2022
arXiv
pre-print
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We then consider graph streams and show that many graph problems are subadditive, including maximum submodular matching, minimum vertex-cover, and maximum k-cover, thereby deriving sliding-window O(1)- ...
Another example is for streaming matrices, where we derive a new sliding-window (√(2)+ϵ)-approximation algorithm for Schatten 4-norm. ...
Using this technique we obtain new sliding-window algorithms for several problems that admit an insertion-only streaming algorithm like estimating a symmetric norm of a frequency-vector or submodular matching ...
arXiv:1904.07957v3
fatcat:4nisfdx7hjczdktls7wqdcuctq
Learning Mixtures of Submodular Functions for Image Collection Summarization
2014
Neural Information Processing Systems
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Interestingly, using non-submodular optimization to learn submodular functions provides the best results. ...
We provide classes of submodular component functions (including some which are instantiated via a deep neural network) over which mixtures may be learnt. ...
A sliding window is moved across an input picture such that every image is divided into 10 × 10 blocks (using a 50% overlap) and the pixels within the window are presented to OverFeat as input. ...
dblp:conf/nips/TschiatschekIWB14
fatcat:7souwvwlpnhrdbobo7rxtptqhe
Tracking Influential Nodes in Time-Decaying Dynamic Interaction Networks
2019
2019 IEEE 35th International Conference on Data Engineering (ICDE)
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Experiments conducted on various real interaction datasets demonstrate that our approach finds near-optimal solutions with speed at least 5 to 15 times faster than baseline methods. ...
Streaming Submodular Optimization (SSO) Methods. ...
Note that our problem requires solving SSO over timedecaying streams, which is more general than sliding-window streams. ...
doi:10.1109/icde.2019.00102
dblp:conf/icde/ZhaoSWLZ19
fatcat:22tawmvp2rew5h5qb2g24bjouy
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