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Machine Learning for Signal Processing

Discriminative Probabilistic Framework for Generalized Multi-Instance Learning


Multiple-instance learning is a framework for learning from data consisting of bags of instances labeled at the bag level. A common assumption in multi-instance learning is that a bag label is positive if and only if at least one instance in the bag is positive. In practice, this assumption may be violated. For example, experts may provide a noisy label to a bag consisting of many instances, to reduce labeling time.

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Authors:
Anh T Pham, Raviv Raich, Xiaoli Fern; Weng K Wong; Xinze Guan
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12 April 2018 - 6:24pm
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Discriminative Probabilistic Framework for Generalized Multi-Instance_ICASSP2018.pdf

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[1] Anh T Pham, Raviv Raich, Xiaoli Fern; Weng K Wong; Xinze Guan, "Discriminative Probabilistic Framework for Generalized Multi-Instance Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2512. Accessed: Apr. 23, 2018.
@article{2512-18,
url = {http://sigport.org/2512},
author = {Anh T Pham; Raviv Raich; Xiaoli Fern; Weng K Wong; Xinze Guan },
publisher = {IEEE SigPort},
title = {Discriminative Probabilistic Framework for Generalized Multi-Instance Learning},
year = {2018} }
TY - EJOUR
T1 - Discriminative Probabilistic Framework for Generalized Multi-Instance Learning
AU - Anh T Pham; Raviv Raich; Xiaoli Fern; Weng K Wong; Xinze Guan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2512
ER -
Anh T Pham, Raviv Raich, Xiaoli Fern; Weng K Wong; Xinze Guan. (2018). Discriminative Probabilistic Framework for Generalized Multi-Instance Learning. IEEE SigPort. http://sigport.org/2512
Anh T Pham, Raviv Raich, Xiaoli Fern; Weng K Wong; Xinze Guan, 2018. Discriminative Probabilistic Framework for Generalized Multi-Instance Learning. Available at: http://sigport.org/2512.
Anh T Pham, Raviv Raich, Xiaoli Fern; Weng K Wong; Xinze Guan. (2018). "Discriminative Probabilistic Framework for Generalized Multi-Instance Learning." Web.
1. Anh T Pham, Raviv Raich, Xiaoli Fern; Weng K Wong; Xinze Guan. Discriminative Probabilistic Framework for Generalized Multi-Instance Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2512

Discriminative Clustering with Cardinality Constraint


Clustering is widely used for exploratory data analysis in a variety of applications. Traditionally clustering is studied as an unsupervised task where no human inputs are provided. A recent trend in clustering is to leverage user provided side information to better infer the clustering structure in data. In this paper, we propose a probabilistic graphical model that allows user to provide as input the desired cluster sizes, namely the cardinality constraints. Our model also incorporates a flexible mechanism to inject control of the crispness of the clusters.

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12 April 2018 - 6:21pm
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Discriminative Clustering with Cardinality Constraint_ICASSP2018.pdf

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[1] , "Discriminative Clustering with Cardinality Constraint", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2510. Accessed: Apr. 23, 2018.
@article{2510-18,
url = {http://sigport.org/2510},
author = { },
publisher = {IEEE SigPort},
title = {Discriminative Clustering with Cardinality Constraint},
year = {2018} }
TY - EJOUR
T1 - Discriminative Clustering with Cardinality Constraint
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2510
ER -
. (2018). Discriminative Clustering with Cardinality Constraint. IEEE SigPort. http://sigport.org/2510
, 2018. Discriminative Clustering with Cardinality Constraint. Available at: http://sigport.org/2510.
. (2018). "Discriminative Clustering with Cardinality Constraint." Web.
1. . Discriminative Clustering with Cardinality Constraint [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2510

Discriminative Clustering with Cardinality Constraint


Clustering is widely used for exploratory data analysis in a variety of applications. Traditionally clustering is studied as an unsupervised task where no human inputs are provided. A recent trend in clustering is to leverage user provided side information to better infer the clustering structure in data. In this paper, we propose a probabilistic graphical model that allows user to provide as input the desired cluster sizes, namely the cardinality constraints. Our model also incorporates a flexible mechanism to inject control of the crispness of the clusters.

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Authors:
Anh T Pham; Raviv Raich; Xiaoli Fern
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12 April 2018 - 6:21pm
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Discriminative Clustering with Cardinality Constraint_ICASSP2018.pdf

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[1] Anh T Pham; Raviv Raich; Xiaoli Fern, "Discriminative Clustering with Cardinality Constraint", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2509. Accessed: Apr. 23, 2018.
@article{2509-18,
url = {http://sigport.org/2509},
author = {Anh T Pham; Raviv Raich; Xiaoli Fern },
publisher = {IEEE SigPort},
title = {Discriminative Clustering with Cardinality Constraint},
year = {2018} }
TY - EJOUR
T1 - Discriminative Clustering with Cardinality Constraint
AU - Anh T Pham; Raviv Raich; Xiaoli Fern
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2509
ER -
Anh T Pham; Raviv Raich; Xiaoli Fern. (2018). Discriminative Clustering with Cardinality Constraint. IEEE SigPort. http://sigport.org/2509
Anh T Pham; Raviv Raich; Xiaoli Fern, 2018. Discriminative Clustering with Cardinality Constraint. Available at: http://sigport.org/2509.
Anh T Pham; Raviv Raich; Xiaoli Fern. (2018). "Discriminative Clustering with Cardinality Constraint." Web.
1. Anh T Pham; Raviv Raich; Xiaoli Fern. Discriminative Clustering with Cardinality Constraint [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2509

A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation


A domain adaptation method for urban scene segmentation is proposed in this work. We develop a fully convolutional tri-branch network, where two branches assign pseudo labels to images in the unlabeled target domain while the third branch is trained with supervision based on images in the pseudo-labeled target domain. The re-labeling and re-training processes alternate. With this design, the tri-branch network learns target-specific discriminative representations progressively and, as a result, the cross-domain capability of the segmenter improves.

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Authors:
Junting Zhang, Chen Liang, C.-C. Jay Kuo
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12 April 2018 - 5:32pm
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A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation-Poster

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[1] Junting Zhang, Chen Liang, C.-C. Jay Kuo, "A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2503. Accessed: Apr. 23, 2018.
@article{2503-18,
url = {http://sigport.org/2503},
author = {Junting Zhang; Chen Liang; C.-C. Jay Kuo },
publisher = {IEEE SigPort},
title = {A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation},
year = {2018} }
TY - EJOUR
T1 - A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation
AU - Junting Zhang; Chen Liang; C.-C. Jay Kuo
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2503
ER -
Junting Zhang, Chen Liang, C.-C. Jay Kuo. (2018). A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation. IEEE SigPort. http://sigport.org/2503
Junting Zhang, Chen Liang, C.-C. Jay Kuo, 2018. A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation. Available at: http://sigport.org/2503.
Junting Zhang, Chen Liang, C.-C. Jay Kuo. (2018). "A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation." Web.
1. Junting Zhang, Chen Liang, C.-C. Jay Kuo. A Fully Convolutional Tri-branch Network (FCTN) For Domain Adaptation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2503

Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space


In this paper, we study the problem of locating a predefined sequence of patterns in a time series. In particular, the studied scenario assumes a theoretical model is available that contains the expected locations of the patterns. This problem is found in several contexts, and it is commonly solved by first synthesizing a time series from the model, and then aligning it to the true time series through dynamic time warping. We propose a technique that increases the similarity of both time series before aligning them, by mapping them into a latent correlation space.

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Authors:
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori
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12 April 2018 - 11:50am
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Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space

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[1] Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori, "Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2412. Accessed: Apr. 23, 2018.
@article{2412-18,
url = {http://sigport.org/2412},
author = {Steven Van Vaerenbergh; Ignacio Santamaría; Víctor Elvira; Matteo Salvatori },
publisher = {IEEE SigPort},
title = {Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space},
year = {2018} }
TY - EJOUR
T1 - Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space
AU - Steven Van Vaerenbergh; Ignacio Santamaría; Víctor Elvira; Matteo Salvatori
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2412
ER -
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori. (2018). Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space. IEEE SigPort. http://sigport.org/2412
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori, 2018. Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space. Available at: http://sigport.org/2412.
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori. (2018). "Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space." Web.
1. Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori. Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2412

Outlier-Robust Matrix Completion via lp-Minimization


Matrix completion refers to the recovery of a low‐rank matrix from only a subset of its possibly noisy entries, and has a variety of important applications such as collaborative filtering, image inpainting and restoration, system identification, node localization and genotype imputation. It is because many real-world signals can be approximated by a matrix whose rank is much smaller than the row and column numbers. Most techniques for matrix completion in the literature assume Gaussian noise and thus they are not robust to outliers.

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Authors:
Wen-Jun Zeng, Hing Cheung So
Submitted On:
2 March 2018 - 1:57am
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rmp.pdf

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[1] Wen-Jun Zeng, Hing Cheung So, "Outlier-Robust Matrix Completion via lp-Minimization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2373. Accessed: Apr. 23, 2018.
@article{2373-18,
url = {http://sigport.org/2373},
author = {Wen-Jun Zeng; Hing Cheung So },
publisher = {IEEE SigPort},
title = {Outlier-Robust Matrix Completion via lp-Minimization},
year = {2018} }
TY - EJOUR
T1 - Outlier-Robust Matrix Completion via lp-Minimization
AU - Wen-Jun Zeng; Hing Cheung So
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2373
ER -
Wen-Jun Zeng, Hing Cheung So. (2018). Outlier-Robust Matrix Completion via lp-Minimization. IEEE SigPort. http://sigport.org/2373
Wen-Jun Zeng, Hing Cheung So, 2018. Outlier-Robust Matrix Completion via lp-Minimization. Available at: http://sigport.org/2373.
Wen-Jun Zeng, Hing Cheung So. (2018). "Outlier-Robust Matrix Completion via lp-Minimization." Web.
1. Wen-Jun Zeng, Hing Cheung So. Outlier-Robust Matrix Completion via lp-Minimization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2373

ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING


In this paper we examine a technique for developing prognostic image characteristics, termed radiomics, for non-small cell lung cancer based on a tumour edge region-based analysis. Texture features were extracted from the rind of the tumour in a publicly available 3D CT data set to predict two-year survival. The derived models were compared against the previous methods of training radiomic signatures that are descriptive of the whole tumour volume. Radiomic features derived solely from regions external, but neighbouring, the tumour were shown to also have prognostic value.

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Authors:
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller
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11 December 2017 - 5:01pm
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GlobalSIP - Conference Presentation v2.pdf

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[1] Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller, "ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2371. Accessed: Apr. 23, 2018.
@article{2371-17,
url = {http://sigport.org/2371},
author = {Alanna Vial; David Stirling; Matthew Field; Montserrat Ros; Christian Ritz; Martin Carolan; Lois Hollowayn; Alexis A. Miller },
publisher = {IEEE SigPort},
title = {ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING},
year = {2017} }
TY - EJOUR
T1 - ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING
AU - Alanna Vial; David Stirling; Matthew Field; Montserrat Ros; Christian Ritz; Martin Carolan; Lois Hollowayn; Alexis A. Miller
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2371
ER -
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller. (2017). ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING. IEEE SigPort. http://sigport.org/2371
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller, 2017. ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING. Available at: http://sigport.org/2371.
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller. (2017). "ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING." Web.
1. Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz, Martin Carolan, Lois Hollowayn, Alexis A. Miller. ASSESSING THE PROGNOSTIC IMPACT OF 3D CT IMAGE TUMOUR RIND TEXTURE FEATURES ON LUNG CANCER SURVIVAL MODELLING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2371

Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins

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Authors:
Brayden Hollis, Stacy Patterson, Jeff Trinkle
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14 November 2017 - 12:55pm
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GlobalSIP_Poster.pdf

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[1] Brayden Hollis, Stacy Patterson, Jeff Trinkle, "Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2351. Accessed: Apr. 23, 2018.
@article{2351-17,
url = {http://sigport.org/2351},
author = {Brayden Hollis; Stacy Patterson; Jeff Trinkle },
publisher = {IEEE SigPort},
title = {Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins},
year = {2017} }
TY - EJOUR
T1 - Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins
AU - Brayden Hollis; Stacy Patterson; Jeff Trinkle
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2351
ER -
Brayden Hollis, Stacy Patterson, Jeff Trinkle. (2017). Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins. IEEE SigPort. http://sigport.org/2351
Brayden Hollis, Stacy Patterson, Jeff Trinkle, 2017. Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins. Available at: http://sigport.org/2351.
Brayden Hollis, Stacy Patterson, Jeff Trinkle. (2017). "Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins." Web.
1. Brayden Hollis, Stacy Patterson, Jeff Trinkle. Adaptive Basis Selection for Compressed Sensing in Robotic Tactile Skins [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2351

Cepstrum Coefficients Based Sleep Stage Classification

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Authors:
Emin Argun Oral, Muhammet Mustafa Codur, Ibrahim Yucel Ozbek
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14 November 2017 - 10:10am
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Cepstrum Coefficients Sleep Classification

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[1] Emin Argun Oral, Muhammet Mustafa Codur, Ibrahim Yucel Ozbek, "Cepstrum Coefficients Based Sleep Stage Classification", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2344. Accessed: Apr. 23, 2018.
@article{2344-17,
url = {http://sigport.org/2344},
author = {Emin Argun Oral; Muhammet Mustafa Codur; Ibrahim Yucel Ozbek },
publisher = {IEEE SigPort},
title = {Cepstrum Coefficients Based Sleep Stage Classification},
year = {2017} }
TY - EJOUR
T1 - Cepstrum Coefficients Based Sleep Stage Classification
AU - Emin Argun Oral; Muhammet Mustafa Codur; Ibrahim Yucel Ozbek
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2344
ER -
Emin Argun Oral, Muhammet Mustafa Codur, Ibrahim Yucel Ozbek. (2017). Cepstrum Coefficients Based Sleep Stage Classification. IEEE SigPort. http://sigport.org/2344
Emin Argun Oral, Muhammet Mustafa Codur, Ibrahim Yucel Ozbek, 2017. Cepstrum Coefficients Based Sleep Stage Classification. Available at: http://sigport.org/2344.
Emin Argun Oral, Muhammet Mustafa Codur, Ibrahim Yucel Ozbek. (2017). "Cepstrum Coefficients Based Sleep Stage Classification." Web.
1. Emin Argun Oral, Muhammet Mustafa Codur, Ibrahim Yucel Ozbek. Cepstrum Coefficients Based Sleep Stage Classification [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2344

COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET

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Authors:
A. Omer Saritac, C. Tekin
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14 November 2017 - 7:11am
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presentation_1.pdf

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[1] A. Omer Saritac, C. Tekin, "COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2342. Accessed: Apr. 23, 2018.
@article{2342-17,
url = {http://sigport.org/2342},
author = {A. Omer Saritac; C. Tekin },
publisher = {IEEE SigPort},
title = {COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET},
year = {2017} }
TY - EJOUR
T1 - COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET
AU - A. Omer Saritac; C. Tekin
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2342
ER -
A. Omer Saritac, C. Tekin. (2017). COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET. IEEE SigPort. http://sigport.org/2342
A. Omer Saritac, C. Tekin, 2017. COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET. Available at: http://sigport.org/2342.
A. Omer Saritac, C. Tekin. (2017). "COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET." Web.
1. A. Omer Saritac, C. Tekin. COMBINATORIAL MULTI-ARMED BANDIT PROBLEM WITH PROBABILISTICALLY TRIGGERED ARMS: A CASE WITH BOUNDED REGRET [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2342

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