The Experts below are selected from a list of 90 Experts worldwide ranked by ideXlab platform
Xiaojin Zhu - One of the best experts on this subject based on the ideXlab platform.
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an optimal control approach to Sequential Machine teaching
International Conference on Artificial Intelligence and Statistics, 2019Co-Authors: Laurent Lessard, Xuezhou Zhang, Xiaojin ZhuAbstract:Given a Sequential learning algorithm and a target model, Sequential Machine teaching aims to find the shortest training sequence to drive the learning algorithm to the target model. We present the first principled way to find such shortest training sequences. Our key insight is to formulate Sequential Machine teaching as a time-optimal control problem. This allows us to solve Sequential teaching by leveraging key theoretical and computational tools developed over the past 60 years in the optimal control community. Specifically, we study the Pontryagin Maximum Principle, which yields a necessary condition for opti- mality of a training sequence. We present analytic, structural, and numerical implica- tions of this approach on a case study with a least-squares loss function and gradient de- scent learner. We compute optimal train- ing sequences for this problem, and although the sequences seem circuitous, we find that they can vastly outperform the best available heuristics for generating training sequences.
Venugopal V Veeravalli - One of the best experts on this subject based on the ideXlab platform.
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adaptive Sequential Machine learning
Sequential Analysis, 2019Co-Authors: Craig Wilson, Venugopal V VeeravalliAbstract:A framework previously introduced in Wilson et al. (2018) for solving a sequence of stochastic optimization problems with bounded changes in the minimizers is extended and applied to Machine learni...
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adaptive Sequential Machine learning
arXiv: Learning, 2019Co-Authors: Craig Wilson, Venugopal V VeeravalliAbstract:A framework previously introduced in [3] for solving a sequence of stochastic optimization problems with bounded changes in the minimizers is extended and applied to Machine learning problems such as regression and classification. The stochastic optimization problems arising in these Machine learning problems is solved using algorithms such as stochastic gradient descent (SGD). A method based on estimates of the change in the minimizers and properties of the optimization algorithm is introduced for adaptively selecting the number of samples at each time step to ensure that the excess risk, i.e., the expected gap between the loss achieved by the approximate minimizer produced by the optimization algorithm and the exact minimizer, does not exceed a target level. A bound is developed to show that the estimate of the change in the minimizers is non-trivial provided that the excess risk is small enough. Extensions relevant to the Machine learning setting are considered, including a cost-based approach to select the number of samples with a cost budget over a fixed horizon, and an approach to applying cross-validation for model selection. Finally, experiments with synthetic and real data are used to validate the algorithms.
Laurent Lessard - One of the best experts on this subject based on the ideXlab platform.
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an optimal control approach to Sequential Machine teaching
International Conference on Artificial Intelligence and Statistics, 2019Co-Authors: Laurent Lessard, Xuezhou Zhang, Xiaojin ZhuAbstract:Given a Sequential learning algorithm and a target model, Sequential Machine teaching aims to find the shortest training sequence to drive the learning algorithm to the target model. We present the first principled way to find such shortest training sequences. Our key insight is to formulate Sequential Machine teaching as a time-optimal control problem. This allows us to solve Sequential teaching by leveraging key theoretical and computational tools developed over the past 60 years in the optimal control community. Specifically, we study the Pontryagin Maximum Principle, which yields a necessary condition for opti- mality of a training sequence. We present analytic, structural, and numerical implica- tions of this approach on a case study with a least-squares loss function and gradient de- scent learner. We compute optimal train- ing sequences for this problem, and although the sequences seem circuitous, we find that they can vastly outperform the best available heuristics for generating training sequences.
Salim Hariri - One of the best experts on this subject based on the ideXlab platform.
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wireless anomaly detection based on ieee 802 11 behavior analysis
IEEE Transactions on Information Forensics and Security, 2015Co-Authors: Hamid Alipour, Youssif Alnashif, Pratik Satam, Salim HaririAbstract:Wireless communication networks are pervading every aspect of our lives due to their fast, easy, and inexpensive deployment. They are becoming ubiquitous and have been widely used to transfer critical information, such as banking accounts, credit cards, e-mails, and social network credentials. The more pervasive the wireless technology is going to be, the more important its security issue will be. Whereas the current security protocols for wireless networks have addressed the privacy and confidentiality issues, there are unaddressed vulnerabilities threatening their availability and integrity (e.g., denial of service, session hijacking, and MAC address spoofing attacks). In this paper, we describe an anomaly based intrusion detection system for the IEEE 802.11 wireless networks based on behavioral analysis to detect deviations from normal behaviors that are triggered by wireless network attacks. Our anomaly behavior analysis of the 802.11 protocols is based on monitoring the n-consecutive transitions of the protocol state Machine. We apply Sequential Machine learning techniques to model the n-transition patterns in the protocol and characterize the probabilities of these transitions being normal. We have implemented several experiments to evaluate our system performance. By cross validating the system over two different wireless channels, we have achieved a low false alarm rate (<0.1%). We have also evaluated our approach against an attack library of known wireless attacks and has achieved more than 99% detection rate.
Craig Wilson - One of the best experts on this subject based on the ideXlab platform.
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adaptive Sequential Machine learning
Sequential Analysis, 2019Co-Authors: Craig Wilson, Venugopal V VeeravalliAbstract:A framework previously introduced in Wilson et al. (2018) for solving a sequence of stochastic optimization problems with bounded changes in the minimizers is extended and applied to Machine learni...
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adaptive Sequential Machine learning
arXiv: Learning, 2019Co-Authors: Craig Wilson, Venugopal V VeeravalliAbstract:A framework previously introduced in [3] for solving a sequence of stochastic optimization problems with bounded changes in the minimizers is extended and applied to Machine learning problems such as regression and classification. The stochastic optimization problems arising in these Machine learning problems is solved using algorithms such as stochastic gradient descent (SGD). A method based on estimates of the change in the minimizers and properties of the optimization algorithm is introduced for adaptively selecting the number of samples at each time step to ensure that the excess risk, i.e., the expected gap between the loss achieved by the approximate minimizer produced by the optimization algorithm and the exact minimizer, does not exceed a target level. A bound is developed to show that the estimate of the change in the minimizers is non-trivial provided that the excess risk is small enough. Extensions relevant to the Machine learning setting are considered, including a cost-based approach to select the number of samples with a cost budget over a fixed horizon, and an approach to applying cross-validation for model selection. Finally, experiments with synthetic and real data are used to validate the algorithms.
