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Online selection of intervals and t -intervals

2013
*
Information and Computation
*

We consider the problems

doi:10.1016/j.ic.2013.10.004
fatcat:zlo6ighaqbgkjkc3vugwb5hjqm
*of**online**selection**of**intervals**and**t*-*intervals*, which show up in Video-on-Demand services, high speed networks*and*molecular biology, among others. ... We derive lower bounds*and*(almost) matching upper bounds on the competitive ratios*of*randomized algorithms for*selecting**intervals*, 2-*intervals**and*tintervals, for any*t*> 2. ... The competitive ratio*of*A is defined as sup σ OP*T*(σ) A(σ) , where σ is an input sequence,*and*OP*T*(σ), A(σ) are the number*of**t*-*intervals**selected*by OP*T**and*A, respectively. ...##
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Online Selection of Intervals and t-Intervals
[chapter]

2010
*
Lecture Notes in Computer Science
*

We consider the problems

doi:10.1007/978-3-642-13731-0_36
fatcat:w5ytdj6drbdpllcvppfjgoiccy
*of**online**selection**of**intervals**and**t*-*intervals*, which show up in Video-on-Demand services, high speed networks*and*molecular biology, among others. ... We derive lower bounds*and*(almost) matching upper bounds on the competitive ratios*of*randomized algorithms for*selecting**intervals*, 2-*intervals**and*tintervals, for any*t*> 2. ... The competitive ratio*of*A is defined as sup σ OP*T*(σ) A(σ) , where σ is an input sequence,*and*OP*T*(σ), A(σ) are the number*of**t*-*intervals**selected*by OP*T**and*A, respectively. ...##
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Online Control of the False Coverage Rate and False Sign Rate
[article]

2019
*
arXiv
*
pre-print

Last, all

arXiv:1905.01059v1
fatcat:lm5clvg2kzdr7eo5er5zuzrhcu
*of*our methodology applies equally well to*online*FCR control for prediction*intervals*, having particular implications for assumption-free*selective*conformal inference. ... In the*online*setting, there is an infinite sequence*of*fixed unknown parameters θ_*t*ordered by time. ... the CI for θ*t*is dependent only on X*t**and*independent*of*all other X i ,*and*so is the*interval*if constructed. ...##
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Online Retweet Recommendation with Item Count Limits

2014
*
2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)
*

In order to help the management

doi:10.1109/wi-iat.2014.45
dblp:conf/webi/ZhaoT14
fatcat:fcxxcprltvho3lca4cedsjhufi
*of*such Twitter accounts, we developed a system that reads a sequence*of*tweets from the friends one by one,*and**select*a given number*of*(or less) tweets in an*online*( ... The former two are truly*online*algorithms*and*the latter two are near-*online*algorithms. ... In other words,D ′ (u,*T*) is the top-c tweets posted by the friends*of*u during*T*. r(u,*T*) represents the ratio*of*total value*of*tweets*selected*by an*online*algorithm*and*by the optimal offline algorithm ...##
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Computing the Median with Uncertainty

2003
*
SIAM journal on computing (Print)
*

We focus on the

doi:10.1137/s0097539701395668
fatcat:zdnul47bhzfw7ml2kaxrsz5uam
*selection*function f which returns the value*of*the kth smallest argument. We present optimal o ine*and**online*algorithms for this problem. ... It is desired to compute a function f X 1 ; : : : ; X n where X 1 ; : : : ; X n are unknown, but guaranteed to lie in speci ed*intervals*I 1 ; : : : ; I n . ... Theorem 3 With arbitrary costs, the greedy polynomial*online**selection*algorithm achieves the cost V*of*Proposition 1,*and*is therefore optimal. ...##
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Computing the median with uncertainty

2000
*
Proceedings of the thirty-second annual ACM symposium on Theory of computing - STOC '00
*

We focus on the

doi:10.1145/335305.335386
dblp:conf/stoc/FederMPOW00
fatcat:2e6fs6rwpnepjp7rzdmwgldnnm
*selection*function f which returns the value*of*the kth smallest argument. We present optimal o ine*and**online*algorithms for this problem. ... It is desired to compute a function f X 1 ; : : : ; X n where X 1 ; : : : ; X n are unknown, but guaranteed to lie in speci ed*intervals*I 1 ; : : : ; I n . ... Theorem 3 With arbitrary costs, the greedy polynomial*online**selection*algorithm achieves the cost V*of*Proposition 1,*and*is therefore optimal. ...##
###
Improved randomized results for the interval selection problem

2010
*
Theoretical Computer Science
*

*Online*

*interval*

*selection*is a problem in which

*intervals*arrive one by one, sorted by their left endpoints. Each

*interval*has a length

*and*a non-negative weight associated with it. ... The goal is to

*select*a non-overlapping set

*of*

*intervals*with maximal total weight

*and*run them to completion. ... See [8, 9] for recent surveys on (offline

*and*

*online*)

*interval*

*selection*problems. ...

##
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Near-optimal online multiselection in internal and external memory

2016
*
Journal of Discrete Algorithms
*

We introduce an

doi:10.1016/j.jda.2015.11.001
fatcat:cn5ir4iwcnhkpdwuvgkr4sizti
*online*version*of*the multiselection problem, in which q*selection*queries are requested on an unsorted array*of*n elements. ... We also extend it to support searches, insertions,*and*deletions*of*elements efficiently. ... Given an (unsorted) array A*of*n elements*and*a sequence R*of*q*online**selection**and*search queries*of*which q are search, we provide • a randomized*online*algorithm that performs the queries using B(S ...##
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Online Learning Adaptive to Dynamic and Adversarial Environments

2018
*
2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
*

Performance is analyzed in terms

doi:10.1109/spawc.2018.8445874
dblp:conf/spawc/ShenCG18
fatcat:kigs77vwxjasfpl4kl5ec7stwm
*of*both static*and*dynamic regret. ... Leveraging the random feature approximation*and*its recent orthogonality-promoting variant, the present contribution develops an*online*multi-kernel learning scheme to infer the intended nonlinear function ... Tianyi Chen is also supported by the Doctoral Dissertation Fellowship from the University*of*Minnesota. ...##
###
An online supervised learning algorithm based on triple spikes for spiking neural networks
[article]

2019
*
arXiv
*
pre-print

Relationship among desired output, actual output

arXiv:1901.01549v2
fatcat:mjrbhc56kzezfbpgaep2omvhiq
*and*input spike trains is firstly analyzed*and*synthesized to simply*select*a unit*of*pair-spike for a direct regulation. ... Based on an*online*regulative mechanism*of*biological synapses, this paper proposes an*online*supervised learning algorithm*of*multiple spike trains for spiking neural networks. ... Acknowledg ments Firstly, we thank editors*and*reviewers for this manuscript. ...##
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Theory and Implementation of Online Multiselection Algorithms
[chapter]

2013
*
Lecture Notes in Computer Science
*

We introduce a new

doi:10.1007/978-3-642-40450-4_10
fatcat:wsib4ffh6jggfohha7gtqyccym
*online*algorithm for the multiselection problem which performs a sequence*of**selection*queries on a given unsorted array. ... We show that our*online*algorithm is 1-competitive in terms*of*data comparisons. ... If the pivot*selection*method is c-balanced, then B(P*t*) = B(S*t*) + O(n). Proof. We sketch the proof*and*defer the full details to the journal version*of*the paper. ...##
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Randomized Lower Bounds for Online Path Coloring
[chapter]

1998
*
Lecture Notes in Computer Science
*

We show that no randomized algorithm for

doi:10.1007/3-540-49543-6_19
fatcat:stroe52y55f2jcpfu64dsuyeba
*online*coloring*of**interval*graphs achieves a competitive ratio strictly better than the best known deterministic algorithm KT81]. ... We study the power*of*randomization in the design*of**online*graph coloring algorithms. ... With probability 1=2, corresponding to the*selection**of*con guration*T*3 ,*interval*I 3 overlaps all the*intervals**of*2*and*3 . For every color*of*C 2 C 3 = C !? ...##
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Online Scheduling with Interval Conflicts

2012
*
Theory of Computing Systems
*

In the problem

doi:10.1007/s00224-012-9408-1
fatcat:32elskgiirdchbwkswusc6uwfe
*of*Scheduling with*Interval*Conflicts, there is a ground set*of*items indexed by integers,*and*the input is a collection*of*conflicts, each containing all the items whose index lies within ... A scheduling algorithm must*select*, from each conflict, at most one survivor item,*and*the goal is to maximize the number (or weight)*of*items that survive all the conflicts they are involved in. ... Similarly, if a > o the*interval*[*t*, o] is positive, because [*t*, o] ∩ A q = ∅,*and*all positive*intervals*between I*t**and*I*t*−1 are left unchanged. ...##
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Online Selection Problems against Constrained Adversary

2021
*
International Conference on Machine Learning
*

We revisit classical

dblp:conf/icml/JiangLT021
fatcat:dlfhneo3ivbfrapy66k6qnt4o4
*online**selection*problems under the constrained adversary model. ... Inspired by a recent line*of*work in*online*algorithms with predictions, we study the constrained adversary model that utilizes predictions from a different perspective. ... Shanghai University*of*Finance*and*Economics (IRTSHUFE)*and*the Fundamental Research Funds for the Central Universities. ...##
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Access Point Selection for Improving Throughput Fairness in Wireless LANs

2007
*
2007 10th IFIP/IEEE International Symposium on Integrated Network Management
*

We investigate the problem

doi:10.1109/inm.2007.374812
dblp:conf/im/SirisE07
fatcat:twuwkbka45bcxijpujioxydvi4
*of*access point*selection*in wireless LANs based on the IEEE 802.11 standard, when a station is within the vicinity*of*more than one access points. ... the minimum contention window,*and*it can be implemented solely at the wireless stations, which passively monitor the activity*of*each access point's channel, without requiring modifications to the access ... We denote the length*of*each*interval*type as*T*suc ,*T*col ,*and**T*idl , respectively. The duration*of*each time*interval*depends on the physical layer encoding*and*the MAC layer operations. ...
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