Sorry, you need to enable JavaScript to visit this website.

Sequential learning; sequential decision methods (MLR-SLER)

SEQUENTIAL MAXIMUM MARGIN CLASSIFIERS FOR PARTIALLY LABELED DATA

Paper Details

Authors:
Elizabeth Hou, Alfred O. Hero
Submitted On:
18 April 2018 - 4:55pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster.pdf

(6 downloads)

Keywords

Subscribe

[1] Elizabeth Hou, Alfred O. Hero, "SEQUENTIAL MAXIMUM MARGIN CLASSIFIERS FOR PARTIALLY LABELED DATA", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2973. Accessed: Apr. 27, 2018.
@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.

Paper Details

Authors:
Ravi Kiran Raman, Srilakshmi Pattabiraman
Submitted On:
18 April 2018 - 1:02pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP_draft2.pptx

(10 downloads)

Keywords

Subscribe

[1] Ravi Kiran Raman, Srilakshmi Pattabiraman, "SELFISH LEARNING: LEVERAGING THE GREED IN SOCIAL LEARNING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2860. Accessed: Apr. 27, 2018.
@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

Paper Details

Authors:
Vaibhav Srivastava
Submitted On:
13 April 2018 - 3:39pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Reverdy-ICASSP18.pdf

(6 downloads)

Keywords

Subscribe

[1] Vaibhav Srivastava, "MULTI-ARMED BANDITS FOR HUMAN-MACHINE DECISION MAKING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2753. Accessed: Apr. 27, 2018.
@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.

Paper Details

Authors:
Christopher Schymura, Juan Diego Rios Grajales, Dorothea Kolossa
Submitted On:
1 March 2017 - 5:16am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster_icassp2017.pdf

(322 downloads)

Keywords

Subscribe

[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: Apr. 27, 2018.
@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
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

GlobalSIP_talk.pdf

(209 downloads)

Keywords

Subscribe

[1] Anit Kumar Sahu, Soummya Kar, "Distributed Sequence Prediction: A consensus+innovations approach", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1357. Accessed: Apr. 27, 2018.
@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
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster_ICASSP2016.pdf

(273 downloads)

Keywords

Subscribe

[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: Apr. 27, 2018.
@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

Paper Details

Authors:
Dimitrios Berberidis, Georgios B. Giannakis
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

Globalsip2015.pdf

(694 downloads)

Keywords

Subscribe

[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: Apr. 27, 2018.
@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

GlobalSIP_slides_AEK


This paper considers unconstrained convex optimiza- tion problems with time-varying objective functions. We propose algorithms with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and correction steps, while sampling the problem data at a constant rate of 1{h, where h is the length of the sampling interval. The prediction step is derived by analyzing the iso-residual dynamics of the optimality conditions.

globalsip.pdf

PDF icon globalsip.pdf (1399 downloads)

Paper Details

Authors:
Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

globalsip.pdf

(1399 downloads)

Keywords

Subscribe

[1] Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro, "GlobalSIP_slides_AEK", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/367. Accessed: Apr. 27, 2018.
@article{367-15,
url = {http://sigport.org/367},
author = {Andrea Simonetto; Aryan Mokhtari; Geert Leus; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {GlobalSIP_slides_AEK},
year = {2015} }
TY - EJOUR
T1 - GlobalSIP_slides_AEK
AU - Andrea Simonetto; Aryan Mokhtari; Geert Leus; Alejandro Ribeiro
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/367
ER -
Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro. (2015). GlobalSIP_slides_AEK. IEEE SigPort. http://sigport.org/367
Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro, 2015. GlobalSIP_slides_AEK. Available at: http://sigport.org/367.
Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro. (2015). "GlobalSIP_slides_AEK." Web.
1. Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro. GlobalSIP_slides_AEK [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/367

CRH: A Simple Benchmark Approach to Continuous Hashing


gsip_mc.pdf

PDF icon gsip_mc.pdf (439 downloads)

gsip_mc.pdf

PDF icon gsip_mc.pdf (318 downloads)

Paper Details

Authors:
Submitted On:
23 February 2016 - 1:43pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

gsip_mc.pdf

(439 downloads)

gsip_mc.pdf

(318 downloads)

Keywords

Subscribe

[1] , "CRH: A Simple Benchmark Approach to Continuous Hashing", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/248. Accessed: Apr. 27, 2018.
@article{248-15,
url = {http://sigport.org/248},
author = { },
publisher = {IEEE SigPort},
title = {CRH: A Simple Benchmark Approach to Continuous Hashing},
year = {2015} }
TY - EJOUR
T1 - CRH: A Simple Benchmark Approach to Continuous Hashing
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/248
ER -
. (2015). CRH: A Simple Benchmark Approach to Continuous Hashing. IEEE SigPort. http://sigport.org/248
, 2015. CRH: A Simple Benchmark Approach to Continuous Hashing. Available at: http://sigport.org/248.
. (2015). "CRH: A Simple Benchmark Approach to Continuous Hashing." Web.
1. . CRH: A Simple Benchmark Approach to Continuous Hashing [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/248

A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation


Precise estimation of synchronization parameters is essential for reliable
data detection in digital communications and phase errors can
result in significant performance degradation. The literature on estimation
of synchronization parameters, including the carrier frequency
offset, are based on approximations or heuristics because
the optimal estimation problem is analytically intractable for most
cases of interest. We develop an online Bayesian inference procedure
for blind estimation of the frequency offset, for arbitrary signal

Paper Details

Authors:
Keith W. Forsythe
Submitted On:
23 February 2016 - 1:43pm
Short Link:
Type:
Event:

Document Files

SeqBayesInfBlindFreqEst_sigport.pdf

(508 downloads)

Keywords

Additional Categories

Subscribe

[1] Keith W. Forsythe, "A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/213. Accessed: Apr. 27, 2018.
@article{213-15,
url = {http://sigport.org/213},
author = {Keith W. Forsythe },
publisher = {IEEE SigPort},
title = {A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation},
year = {2015} }
TY - EJOUR
T1 - A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation
AU - Keith W. Forsythe
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/213
ER -
Keith W. Forsythe. (2015). A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation. IEEE SigPort. http://sigport.org/213
Keith W. Forsythe, 2015. A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation. Available at: http://sigport.org/213.
Keith W. Forsythe. (2015). "A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation." Web.
1. Keith W. Forsythe. A Sequential Bayesian Inference Framework for Blind Frequency Offset Estimation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/213