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Batch and online learning algorithms for nonconvex neyman-pearson classification

Gilles Gasso, Aristidis Pappaioannou, Marina Spivak, Léon Bottou
2011 ACM Transactions on Intelligent Systems and Technology  
We describe and evaluate two algorithms for Neyman-Pearson (NP) classification problem which has been recently shown to be of a particular importance for bipartite ranking problems.  ...  We investigated batch algorithm based on DC programming and stochastic gradient method well suited for large scale datasets. Empirical evidences illustrate the potential of the proposed methods.  ...  CONCLUSION We have proposed a batch approach suited for kernel machines and online learning strategy for large datasets to tackle non-convex Neyman-Pearson classification problem.  ... 
doi:10.1145/1961189.1961200 fatcat:yykk3w5gc5a37cjmvwrlxh3n7i

A Neural Network Approach for Online Nonlinear Neyman-Pearson Classification [article]

Basarbatu Can, Huseyin Ozkan
2020 arXiv   pre-print
As a result, we obtain an expedited online adaptation and powerful nonlinear Neyman-Pearson modeling.  ...  We propose a novel Neyman-Pearson (NP) classifier that is both online and nonlinear as the first time in the literature.  ...  Hence, we construct our method called "NP-NN" in Algorithm 1 that can be used in real time for online nonlinear Neyman-Pearson classification.  ... 
arXiv:2006.08001v2 fatcat:wk6hjb5uirfo5ecijzneib2ede

A Neural Network Approach for Online Nonlinear Neyman-Pearson Classification

Basarbatu Can, Huseyin Ozkan
2020 IEEE Access  
Hence, we construct our method called "NP-NN" in Algorithm 1 that can be used in real time for online nonlinear Neyman-Pearson classification.  ...  SLFN FOR ONLINE NONLINEAR NP CLASSIFICATION In order to learn nonlinear Neyman-Pearson classification boundaries, we use a single hidden layer feed forward neural network (SLFN), illustrated in 1, that  ... 
doi:10.1109/access.2020.3039724 fatcat:4yossxy3afhqlkuy5mkcchmbom

Minimax and Neyman-Pearson Meta-Learning for Outlier Languages [article]

Edoardo Maria Ponti, Rahul Aralikatte, Disha Shrivastava, Siva Reddy, Anders Søgaard
2021 arXiv   pre-print
To increase its robustness to outlier languages, we create two variants of MAML based on alternative criteria: Minimax MAML reduces the maximum risk across languages, while Neyman-Pearson MAML constrains  ...  We report gains for their average and minimum performance across low-resource languages in zero- and few-shot settings, compared to joint multi-source transfer and vanilla MAML.  ...  Acknowledgements We thank the reviewers for their valuable feedback. Rahul Aralikatte and Anders Søgaard are funded by a Google Focused Research Award.  ... 
arXiv:2106.01051v1 fatcat:7mtaaat3e5auvowumypc3vzr5u

Solving Stochastic Optimization with Expectation Constraints Efficiently by a Stochastic Augmented Lagrangian-Type Algorithm [article]

Liwei Zhang and Yule Zhang and Jia Wu and Xiantao Xiao
2022 arXiv   pre-print
Under mild conditions, we show that this algorithm exhibits O(K^-1/2) expected convergence rates for both objective reduction and constraint violation if parameters in the algorithm are properly chosen  ...  This algorithm can be roughly viewed as a hybrid of stochastic approximation and the traditional proximal method of multipliers.  ...  Acknowledgments The authors would like to thank the anonymous reviewers and the associate editor for the valuable comments and suggestions that helped us to greatly improve the quality of the paper.  ... 
arXiv:2106.11577v3 fatcat:ilrz7ikbljgallrpda6iwrova4

Proximally Constrained Methods for Weakly Convex Optimization with Weakly Convex Constraints [article]

Runchao Ma, Qihang Lin, Tianbao Yang
2019 arXiv   pre-print
Optimization models with non-convex constraints arise in many tasks in machine learning, e.g., learning with fairness constraints or Neyman-Pearson classification with non-convex loss.  ...  Each subproblem can be solved by various algorithms for strongly convex optimization.  ...  Although the algorithm by [53] also works well in these instances, it doesn't have any theoretical guarantee for nonconvex constrained optimization problems.  ... 
arXiv:1908.01871v2 fatcat:jb6hdexc5rhc5gqvfw3lntotei

Kernel-Based Learning for Statistical Signal Processing in Cognitive Radio Networks: Theoretical Foundations, Example Applications, and Future Directions

Guoru Ding, Qihui Wu, Yu-Dong Yao, Jinlong Wang, Yingying Chen
2013 IEEE Signal Processing Magazine  
He has published widely in the areas of signal processing for wireless communications and networking.  ...  The authors would like to thank the reviewers for their constructive comments and helpful suggestions and Dr. Nansai Hu China. He is also the cochair of the IEEE Nanjing Section.  ...  online learning [9] .  ... 
doi:10.1109/msp.2013.2251071 fatcat:gsz5mc6nbjcdzezobmqo5wmx2q

Persistency of Excitation for Robustness of Neural Networks [article]

Kamil Nar, S. Shankar Sastry
2019 arXiv   pre-print
When an online learning algorithm is used to estimate the unknown parameters of a model, the signals interacting with the parameter estimates should not decay too quickly for the optimal values to be discovered  ...  While training a neural network, the iterative optimization algorithm involved also creates an online learning problem, and consequently, correct estimation of the optimal parameters requires persistent  ...  trade-off between Type-I and Type-II errors, the ROC curve, and the Neyman-Pearson rule (Poor, 2013; Keener, 2011) .  ... 
arXiv:1911.01043v1 fatcat:5my5dsn2bvgadnadmx4gdrqj5y

Patterns, predictions, and actions: A story about machine learning [article]

Moritz Hardt, Benjamin Recht
2021 arXiv   pre-print
This graduate textbook on machine learning tells a story of how patterns in data support predictions and consequential actions.  ...  Self-contained introductions to causality, the practice of causal inference, sequential decision making, and reinforcement learning equip the reader with concepts and tools to reason about actions and  ...  The Neyman-Pearson Lemma The Neyman-Pearson Lemma, a fundamental lemma of decision theory, will be an important tool for us to establish three important facts.  ... 
arXiv:2102.05242v2 fatcat:wy47g4fojnfuxngklyewtjtqdi

A Comprehensive Survey on Local Differential Privacy

Xingxing Xiong, Shubo Liu, Dan Li, Zhaohui Cai, Xiaoguang Niu
2020 Security and Communication Networks  
Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some  ...  Finally, we identify future research directions and open challenges for LDP.  ...  Moreover, they proposed an Online Convex Optimization framework that is used to design and analyze the algorithms for training machine learning models. Hoeven et al.  ... 
doi:10.1155/2020/8829523 fatcat:xjk3vgyambb5xioc2q5hyr2hua

IEEE Robotics & Automation Society

2012 IEEE robotics & automation magazine  
The fusion center then combines these messages to arrive at an optimal decision in the Neyman-Pearson framework.  ...  An offline (batch) algorithm that combines particle filtering and the expectation maximization is introduced for the identification of such systems.  ... 
doi:10.1109/mra.2012.2230568 fatcat:33actbknxrel3jnag2kx7cncem

IEEE Robotics & Automation Society

2011 IEEE robotics & automation magazine  
The fusion center then combines these messages to arrive at an optimal decision in the Neyman-Pearson framework.  ...  An offline (batch) algorithm that combines particle filtering and the expectation maximization is introduced for the identification of such systems.  ... 
doi:10.1109/mra.2011.941112 fatcat:owvu2behc5hulpcae2dp5myigm

[IEEE Robotics & Automation Society]

2012 IEEE robotics & automation magazine  
The fusion center then combines these messages to arrive at an optimal decision in the Neyman-Pearson framework.  ...  An offline (batch) algorithm that combines particle filtering and the expectation maximization is introduced for the identification of such systems.  ... 
doi:10.1109/mra.2012.2229854 fatcat:rjrxtwk4jbcgjpvjdad6mougsq

IEEE Robotics & Automation Society

2011 IEEE robotics & automation magazine  
The fusion center then combines these messages to arrive at an optimal decision in the Neyman-Pearson framework.  ...  An offline (batch) algorithm that combines particle filtering and the expectation maximization is introduced for the identification of such systems.  ... 
doi:10.1109/mra.2011.943480 fatcat:d2wvloyv6jcbzp2yathd52mx2u