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Sequential learning; sequential decision methods (MLR-SLER)

Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization)


In this paper, we derive generic bounds on the maximum deviations in prediction errors for sequential prediction via an information-theoretic approach. The fundamental bounds are shown to depend only on the conditional entropy of the data point to be predicted given the previous data points. In the asymptotic case, the bounds are achieved if and only if the prediction error is white and uniformly distributed.

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Authors:
Song Fang, Quanyan Zhu
Submitted On:
24 October 2019 - 4:45pm
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[1] Song Fang, Quanyan Zhu, "Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization)", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4890. Accessed: Dec. 08, 2019.
@article{4890-19,
url = {http://sigport.org/4890},
author = {Song Fang; Quanyan Zhu },
publisher = {IEEE SigPort},
title = {Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization)},
year = {2019} }
TY - EJOUR
T1 - Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization)
AU - Song Fang; Quanyan Zhu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4890
ER -
Song Fang, Quanyan Zhu. (2019). Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization). IEEE SigPort. http://sigport.org/4890
Song Fang, Quanyan Zhu, 2019. Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization). Available at: http://sigport.org/4890.
Song Fang, Quanyan Zhu. (2019). "Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization)." Web.
1. Song Fang, Quanyan Zhu. Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization) [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4890

Minimax Active Learning via Minimal Model Capacity


Active learning is a form of machine learning which combines supervised learning and feedback to minimize the training set size, subject to low generalization errors. Since direct optimization of the generalization error is difficult, many heuristics have been developed which lack a firm theoretical foundation. In this paper, a new information theoretic criterion is proposed based on a minimax log-loss regret formulation of the active learning problem. In the first part of this paper, a Redundancy Capacity theorem for active learning is derived along with an optimal learner.

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Authors:
Meir Feder
Submitted On:
16 October 2019 - 4:02pm
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MLSP_2019_Minimax_Active_Learning_via_Minimal_Model_Capacity.pdf

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[1] Meir Feder , "Minimax Active Learning via Minimal Model Capacity", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4876. Accessed: Dec. 08, 2019.
@article{4876-19,
url = {http://sigport.org/4876},
author = {Meir Feder },
publisher = {IEEE SigPort},
title = {Minimax Active Learning via Minimal Model Capacity},
year = {2019} }
TY - EJOUR
T1 - Minimax Active Learning via Minimal Model Capacity
AU - Meir Feder
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4876
ER -
Meir Feder . (2019). Minimax Active Learning via Minimal Model Capacity. IEEE SigPort. http://sigport.org/4876
Meir Feder , 2019. Minimax Active Learning via Minimal Model Capacity. Available at: http://sigport.org/4876.
Meir Feder . (2019). "Minimax Active Learning via Minimal Model Capacity." Web.
1. Meir Feder . Minimax Active Learning via Minimal Model Capacity [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4876

ONLINE ANOMALY DETECTION IN MULTIVARIATE SETTINGS

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Authors:
Mahsa Mozaffari, Yasin Yilmaz
Submitted On:
14 October 2019 - 5:31pm
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mlsp-presentation copy.pptx

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[1] Mahsa Mozaffari, Yasin Yilmaz, "ONLINE ANOMALY DETECTION IN MULTIVARIATE SETTINGS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4871. Accessed: Dec. 08, 2019.
@article{4871-19,
url = {http://sigport.org/4871},
author = {Mahsa Mozaffari; Yasin Yilmaz },
publisher = {IEEE SigPort},
title = {ONLINE ANOMALY DETECTION IN MULTIVARIATE SETTINGS},
year = {2019} }
TY - EJOUR
T1 - ONLINE ANOMALY DETECTION IN MULTIVARIATE SETTINGS
AU - Mahsa Mozaffari; Yasin Yilmaz
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4871
ER -
Mahsa Mozaffari, Yasin Yilmaz. (2019). ONLINE ANOMALY DETECTION IN MULTIVARIATE SETTINGS. IEEE SigPort. http://sigport.org/4871
Mahsa Mozaffari, Yasin Yilmaz, 2019. ONLINE ANOMALY DETECTION IN MULTIVARIATE SETTINGS. Available at: http://sigport.org/4871.
Mahsa Mozaffari, Yasin Yilmaz. (2019). "ONLINE ANOMALY DETECTION IN MULTIVARIATE SETTINGS." Web.
1. Mahsa Mozaffari, Yasin Yilmaz. ONLINE ANOMALY DETECTION IN MULTIVARIATE SETTINGS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4871

Variational and Hierarchical Recurrent Autoencoder


Despite a great success in learning representation for image data, it is challenging to learn the stochastic latent features from natural language based on variational inference. The difficulty in stochastic sequential learning is due to the posterior collapse caused by an autoregressive decoder which is prone to be too strong to learn sufficient latent information during optimization. To compensate this weakness in learning procedure, a sophisticated latent structure is required to assure good convergence so that random features are sufficiently captured for sequential decoding.

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Authors:
Jen-Tzung Chien and Chun-Wei Wang
Submitted On:
7 May 2019 - 8:19pm
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[ICASSP 2019] Variational and hierarchical recurrent autoencoder.pdf

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Icassp19_hier.pdf

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[1] Jen-Tzung Chien and Chun-Wei Wang, "Variational and Hierarchical Recurrent Autoencoder", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3968. Accessed: Dec. 08, 2019.
@article{3968-19,
url = {http://sigport.org/3968},
author = {Jen-Tzung Chien and Chun-Wei Wang },
publisher = {IEEE SigPort},
title = {Variational and Hierarchical Recurrent Autoencoder},
year = {2019} }
TY - EJOUR
T1 - Variational and Hierarchical Recurrent Autoencoder
AU - Jen-Tzung Chien and Chun-Wei Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3968
ER -
Jen-Tzung Chien and Chun-Wei Wang. (2019). Variational and Hierarchical Recurrent Autoencoder. IEEE SigPort. http://sigport.org/3968
Jen-Tzung Chien and Chun-Wei Wang, 2019. Variational and Hierarchical Recurrent Autoencoder. Available at: http://sigport.org/3968.
Jen-Tzung Chien and Chun-Wei Wang. (2019). "Variational and Hierarchical Recurrent Autoencoder." Web.
1. Jen-Tzung Chien and Chun-Wei Wang. Variational and Hierarchical Recurrent Autoencoder [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3968

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: Dec. 08, 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: Dec. 08, 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: Dec. 08, 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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[1] Elizabeth Hou, Alfred O. Hero, "SEQUENTIAL MAXIMUM MARGIN CLASSIFIERS FOR PARTIALLY LABELED DATA", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2973. Accessed: Dec. 08, 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: Dec. 08, 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: Dec. 08, 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

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