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Sequential learning; sequential decision methods (MLR-SLER)

BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS


We study the quickest change point detection for systems with multiple possible post-change models. A change point is the time instant at which the distribution of a random process changes. We consider the case that the post-change model is from a finite set of possible models. Under the Bayesian setting, the objective is to minimize the average detection delay (ADD), subject to upper bounds on the probability of false alarm (PFA). The proposed algorithm is a threshold-based sequential test.

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Authors:
Samrat Nath, Jingxian Wu
Submitted On:
24 November 2018 - 2:22pm
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Change_Detection_GlobalSIP18.pdf

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[1] Samrat Nath, Jingxian Wu, "BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3771. Accessed: Mar. 21, 2019.
@article{3771-18,
url = {http://sigport.org/3771},
author = {Samrat Nath; Jingxian Wu },
publisher = {IEEE SigPort},
title = {BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS},
year = {2018} }
TY - EJOUR
T1 - BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS
AU - Samrat Nath; Jingxian Wu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3771
ER -
Samrat Nath, Jingxian Wu. (2018). BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS. IEEE SigPort. http://sigport.org/3771
Samrat Nath, Jingxian Wu, 2018. BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS. Available at: http://sigport.org/3771.
Samrat Nath, Jingxian Wu. (2018). "BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS." Web.
1. Samrat Nath, Jingxian Wu. BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3771

Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations

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Authors:
Fan Zhang, Qiong Wu, Hao Wang, Yuanming Shi
Submitted On:
20 June 2018 - 9:26pm
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Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations

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[1] Fan Zhang, Qiong Wu, Hao Wang, Yuanming Shi, "Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3276. Accessed: Mar. 21, 2019.
@article{3276-18,
url = {http://sigport.org/3276},
author = {Fan Zhang; Qiong Wu; Hao Wang; Yuanming Shi },
publisher = {IEEE SigPort},
title = {Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations},
year = {2018} }
TY - EJOUR
T1 - Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations
AU - Fan Zhang; Qiong Wu; Hao Wang; Yuanming Shi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3276
ER -
Fan Zhang, Qiong Wu, Hao Wang, Yuanming Shi. (2018). Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations. IEEE SigPort. http://sigport.org/3276
Fan Zhang, Qiong Wu, Hao Wang, Yuanming Shi, 2018. Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations. Available at: http://sigport.org/3276.
Fan Zhang, Qiong Wu, Hao Wang, Yuanming Shi. (2018). "Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations." Web.
1. Fan Zhang, Qiong Wu, Hao Wang, Yuanming Shi. Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3276

Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations

Paper Details

Authors:
Fan Zhang, Qiong Wu, Hao Wang, Yuanming Shi
Submitted On:
20 June 2018 - 9:26pm
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Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations

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[1] Fan Zhang, Qiong Wu, Hao Wang, Yuanming Shi, "Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3275. Accessed: Mar. 21, 2019.
@article{3275-18,
url = {http://sigport.org/3275},
author = {Fan Zhang; Qiong Wu; Hao Wang; Yuanming Shi },
publisher = {IEEE SigPort},
title = {Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations},
year = {2018} }
TY - EJOUR
T1 - Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations
AU - Fan Zhang; Qiong Wu; Hao Wang; Yuanming Shi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3275
ER -
Fan Zhang, Qiong Wu, Hao Wang, Yuanming Shi. (2018). Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations. IEEE SigPort. http://sigport.org/3275
Fan Zhang, Qiong Wu, Hao Wang, Yuanming Shi, 2018. Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations. Available at: http://sigport.org/3275.
Fan Zhang, Qiong Wu, Hao Wang, Yuanming Shi. (2018). "Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations." Web.
1. Fan Zhang, Qiong Wu, Hao Wang, Yuanming Shi. Topological Interference Alignment via Generalized Low-Rank Optimization with Sequential Convex Approximations [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3275

SEQUENTIAL MAXIMUM MARGIN CLASSIFIERS FOR PARTIALLY LABELED DATA

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Authors:
Elizabeth Hou, Alfred O. Hero
Submitted On:
18 April 2018 - 4:55pm
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poster.pdf

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[1] Elizabeth Hou, Alfred O. Hero, "SEQUENTIAL MAXIMUM MARGIN CLASSIFIERS FOR PARTIALLY LABELED DATA", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2973. Accessed: Mar. 21, 2019.
@article{2973-18,
url = {http://sigport.org/2973},
author = {Elizabeth Hou; Alfred O. Hero },
publisher = {IEEE SigPort},
title = {SEQUENTIAL MAXIMUM MARGIN CLASSIFIERS FOR PARTIALLY LABELED DATA},
year = {2018} }
TY - EJOUR
T1 - SEQUENTIAL MAXIMUM MARGIN CLASSIFIERS FOR PARTIALLY LABELED DATA
AU - Elizabeth Hou; Alfred O. Hero
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2973
ER -
Elizabeth Hou, Alfred O. Hero. (2018). SEQUENTIAL MAXIMUM MARGIN CLASSIFIERS FOR PARTIALLY LABELED DATA. IEEE SigPort. http://sigport.org/2973
Elizabeth Hou, Alfred O. Hero, 2018. SEQUENTIAL MAXIMUM MARGIN CLASSIFIERS FOR PARTIALLY LABELED DATA. Available at: http://sigport.org/2973.
Elizabeth Hou, Alfred O. Hero. (2018). "SEQUENTIAL MAXIMUM MARGIN CLASSIFIERS FOR PARTIALLY LABELED DATA." Web.
1. Elizabeth Hou, Alfred O. Hero. SEQUENTIAL MAXIMUM MARGIN CLASSIFIERS FOR PARTIALLY LABELED DATA [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2973

SELFISH LEARNING: LEVERAGING THE GREED IN SOCIAL LEARNING


We introduce a sequential Bayesian binary hypothesis testing problem under social learning, termed selfish learning, where agents work to maximize their individual rewards. In particular, each agent receives a private signal and is aware of decisions made by earlier-acting agents. Beside inferring the underlying hypothesis, agents also decide whether to stop and declare, or pass the inference to the next agent. The employer rewards only correct responses and the reward per worker decreases with the number of employees used for decision making.

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Authors:
Ravi Kiran Raman, Srilakshmi Pattabiraman
Submitted On:
18 April 2018 - 1:02pm
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ICASSP_draft2.pptx

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[1] Ravi Kiran Raman, Srilakshmi Pattabiraman, "SELFISH LEARNING: LEVERAGING THE GREED IN SOCIAL LEARNING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2860. Accessed: Mar. 21, 2019.
@article{2860-18,
url = {http://sigport.org/2860},
author = {Ravi Kiran Raman; Srilakshmi Pattabiraman },
publisher = {IEEE SigPort},
title = {SELFISH LEARNING: LEVERAGING THE GREED IN SOCIAL LEARNING},
year = {2018} }
TY - EJOUR
T1 - SELFISH LEARNING: LEVERAGING THE GREED IN SOCIAL LEARNING
AU - Ravi Kiran Raman; Srilakshmi Pattabiraman
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2860
ER -
Ravi Kiran Raman, Srilakshmi Pattabiraman. (2018). SELFISH LEARNING: LEVERAGING THE GREED IN SOCIAL LEARNING. IEEE SigPort. http://sigport.org/2860
Ravi Kiran Raman, Srilakshmi Pattabiraman, 2018. SELFISH LEARNING: LEVERAGING THE GREED IN SOCIAL LEARNING. Available at: http://sigport.org/2860.
Ravi Kiran Raman, Srilakshmi Pattabiraman. (2018). "SELFISH LEARNING: LEVERAGING THE GREED IN SOCIAL LEARNING." Web.
1. Ravi Kiran Raman, Srilakshmi Pattabiraman. SELFISH LEARNING: LEVERAGING THE GREED IN SOCIAL LEARNING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2860

MULTI-ARMED BANDITS FOR HUMAN-MACHINE DECISION MAKING

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Authors:
Vaibhav Srivastava
Submitted On:
13 April 2018 - 3:39pm
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Reverdy-ICASSP18.pdf

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[1] Vaibhav Srivastava, "MULTI-ARMED BANDITS FOR HUMAN-MACHINE DECISION MAKING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2753. Accessed: Mar. 21, 2019.
@article{2753-18,
url = {http://sigport.org/2753},
author = {Vaibhav Srivastava },
publisher = {IEEE SigPort},
title = {MULTI-ARMED BANDITS FOR HUMAN-MACHINE DECISION MAKING},
year = {2018} }
TY - EJOUR
T1 - MULTI-ARMED BANDITS FOR HUMAN-MACHINE DECISION MAKING
AU - Vaibhav Srivastava
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2753
ER -
Vaibhav Srivastava. (2018). MULTI-ARMED BANDITS FOR HUMAN-MACHINE DECISION MAKING. IEEE SigPort. http://sigport.org/2753
Vaibhav Srivastava, 2018. MULTI-ARMED BANDITS FOR HUMAN-MACHINE DECISION MAKING. Available at: http://sigport.org/2753.
Vaibhav Srivastava. (2018). "MULTI-ARMED BANDITS FOR HUMAN-MACHINE DECISION MAKING." Web.
1. Vaibhav Srivastava. MULTI-ARMED BANDITS FOR HUMAN-MACHINE DECISION MAKING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2753

MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION


This study introduces a machine hearing system for robot audition, which enables a robotic agent to pro-actively minimize the uncertainty of sound source location estimates through motion. The proposed system is based on an active exploration approach, providing a means to model and predict effects of the agent's future motions on localization uncertainty in a probabilistic manner. Particle filtering is used to estimate the posterior probability density function of the source position from binaural measurements, enabling to jointly assess azimuth and distance of the source.

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Authors:
Christopher Schymura, Juan Diego Rios Grajales, Dorothea Kolossa
Submitted On:
1 March 2017 - 5:16am
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poster_icassp2017.pdf

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[1] Christopher Schymura, Juan Diego Rios Grajales, Dorothea Kolossa, "MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1543. Accessed: Mar. 21, 2019.
@article{1543-17,
url = {http://sigport.org/1543},
author = {Christopher Schymura; Juan Diego Rios Grajales; Dorothea Kolossa },
publisher = {IEEE SigPort},
title = {MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION},
year = {2017} }
TY - EJOUR
T1 - MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION
AU - Christopher Schymura; Juan Diego Rios Grajales; Dorothea Kolossa
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1543
ER -
Christopher Schymura, Juan Diego Rios Grajales, Dorothea Kolossa. (2017). MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION. IEEE SigPort. http://sigport.org/1543
Christopher Schymura, Juan Diego Rios Grajales, Dorothea Kolossa, 2017. MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION. Available at: http://sigport.org/1543.
Christopher Schymura, Juan Diego Rios Grajales, Dorothea Kolossa. (2017). "MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION." Web.
1. Christopher Schymura, Juan Diego Rios Grajales, Dorothea Kolossa. MONTE CARLO EXPLORATION FOR ACTIVE BINAURAL LOCALIZATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1543

Distributed Sequence Prediction: A consensus+innovations approach


This paper focuses on the problem of distributed sequence
prediction in a network of sparsely interconnected agents,
where agents collaborate to achieve provably reasonable
predictive performance. An expert assisted online learning
algorithm in a distributed setup of the consensus+innovations
form is proposed, in which the agents update their weights
for the experts’ predictions by simultaneously processing the
latest network losses (innovations) and the cumulative losses
obtained from neighboring agents (consensus). This paper

Paper Details

Authors:
Anit Kumar Sahu, Soummya Kar
Submitted On:
6 December 2016 - 3:37am
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GlobalSIP_talk.pdf

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[1] Anit Kumar Sahu, Soummya Kar, "Distributed Sequence Prediction: A consensus+innovations approach", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1357. Accessed: Mar. 21, 2019.
@article{1357-16,
url = {http://sigport.org/1357},
author = {Anit Kumar Sahu; Soummya Kar },
publisher = {IEEE SigPort},
title = {Distributed Sequence Prediction: A consensus+innovations approach},
year = {2016} }
TY - EJOUR
T1 - Distributed Sequence Prediction: A consensus+innovations approach
AU - Anit Kumar Sahu; Soummya Kar
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1357
ER -
Anit Kumar Sahu, Soummya Kar. (2016). Distributed Sequence Prediction: A consensus+innovations approach. IEEE SigPort. http://sigport.org/1357
Anit Kumar Sahu, Soummya Kar, 2016. Distributed Sequence Prediction: A consensus+innovations approach. Available at: http://sigport.org/1357.
Anit Kumar Sahu, Soummya Kar. (2016). "Distributed Sequence Prediction: A consensus+innovations approach." Web.
1. Anit Kumar Sahu, Soummya Kar. Distributed Sequence Prediction: A consensus+innovations approach [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1357

ONLINE CHANGE DETECTION OF LINEAR REGRESSION MODELS


We consider the problem of quickly detecting an abrupt change of linear coefficients in linear regression models. In particular, the observer sequentially observes a sequence of observations $\{ (x_n; y_n) \}_{n=1}^{\infty}$, which is assumed to obey a linear regression model at each time slot n. Some of the coefficients in the linear model change at a fixed but unknown time $t$. The post-change linear coefficients are unknown to the observer. The observer aims to design an online algorithm to detect the model change based on his sequential observations.

Paper Details

Authors:
Jun Geng, Bingwen Zhang, Lauren M. Huie, Lifeng Lai
Submitted On:
14 April 2018 - 2:11am
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Poster_ICASSP2016.pdf

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[1] Jun Geng, Bingwen Zhang, Lauren M. Huie, Lifeng Lai, "ONLINE CHANGE DETECTION OF LINEAR REGRESSION MODELS", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/890. Accessed: Mar. 21, 2019.
@article{890-16,
url = {http://sigport.org/890},
author = {Jun Geng; Bingwen Zhang; Lauren M. Huie; Lifeng Lai },
publisher = {IEEE SigPort},
title = {ONLINE CHANGE DETECTION OF LINEAR REGRESSION MODELS},
year = {2016} }
TY - EJOUR
T1 - ONLINE CHANGE DETECTION OF LINEAR REGRESSION MODELS
AU - Jun Geng; Bingwen Zhang; Lauren M. Huie; Lifeng Lai
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/890
ER -
Jun Geng, Bingwen Zhang, Lauren M. Huie, Lifeng Lai. (2016). ONLINE CHANGE DETECTION OF LINEAR REGRESSION MODELS. IEEE SigPort. http://sigport.org/890
Jun Geng, Bingwen Zhang, Lauren M. Huie, Lifeng Lai, 2016. ONLINE CHANGE DETECTION OF LINEAR REGRESSION MODELS. Available at: http://sigport.org/890.
Jun Geng, Bingwen Zhang, Lauren M. Huie, Lifeng Lai. (2016). "ONLINE CHANGE DETECTION OF LINEAR REGRESSION MODELS." Web.
1. Jun Geng, Bingwen Zhang, Lauren M. Huie, Lifeng Lai. ONLINE CHANGE DETECTION OF LINEAR REGRESSION MODELS [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/890

Kernel-based low-rank feature extraction on a budget for Big data streams

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Authors:
Dimitrios Berberidis, Georgios B. Giannakis
Submitted On:
23 February 2016 - 1:44pm
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Globalsip2015.pdf

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[1] Dimitrios Berberidis, Georgios B. Giannakis, "Kernel-based low-rank feature extraction on a budget for Big data streams", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/434. Accessed: Mar. 21, 2019.
@article{434-15,
url = {http://sigport.org/434},
author = {Dimitrios Berberidis; Georgios B. Giannakis },
publisher = {IEEE SigPort},
title = {Kernel-based low-rank feature extraction on a budget for Big data streams},
year = {2015} }
TY - EJOUR
T1 - Kernel-based low-rank feature extraction on a budget for Big data streams
AU - Dimitrios Berberidis; Georgios B. Giannakis
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/434
ER -
Dimitrios Berberidis, Georgios B. Giannakis. (2015). Kernel-based low-rank feature extraction on a budget for Big data streams. IEEE SigPort. http://sigport.org/434
Dimitrios Berberidis, Georgios B. Giannakis, 2015. Kernel-based low-rank feature extraction on a budget for Big data streams. Available at: http://sigport.org/434.
Dimitrios Berberidis, Georgios B. Giannakis. (2015). "Kernel-based low-rank feature extraction on a budget for Big data streams." Web.
1. Dimitrios Berberidis, Georgios B. Giannakis. Kernel-based low-rank feature extraction on a budget for Big data streams [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/434

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