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1. Data-driven pricing of demand response

   https://doi.org/10.1109/SmartGridComm.2016.7778765  

   Khezeli, Kia; Bitar, Eilyan (November 2016, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm))

   We consider the setting in which an electric power utility seeks to curtail its peak electricity demand by offering a fixed group of customers a uniform price for reductions in consumption relative to their predetermined baselines. The underlying demand curve, which describes the aggregate reduction in consumption in response to the offered price, is assumed to be affine and subject to unobservable random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility might dynamically adjust its offered prices to maximize its cumulative risk-sensitive payoff over a finite number of T days. In order to do so effectively, the utility must design its pricing policy to balance the tradeoff between the need to learn the unknown demand model (exploration) and maximize its payoff (exploitation) over time. In this paper, we propose such a pricing policy, which is shown to exhibit an expected payoff loss over T days that is at most O(√T), relative to an oracle who knows the underlying demand model. Moreover, the proposed pricing policy is shown to yield a sequence of prices that converge to the oracle optimal prices in the mean square sense. 

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2. Data-driven inference of representation invariants

   https://doi.org/10.1145/3385412.3385967  

   Miltner, Anders; Padhi, Saswat; Millstein, Todd; Walker, David (June 2020, ACM SIGPLAN Conference on Programming Language Design and Implementation)
3. Limits of Data Driven Steganography Detectors

   Dworetzky, E. (June 2023, ACM Information Hiding and Multimedia Security Workshop)

   While deep learning has revolutionized image steganalysis in terms of performance, little is known about how much modern data-driven detectors can still be improved. In this paper, we approach this difficult and currently wide open question by working with artificial but realistic looking images with a known statistical model that allows us to compute the detectability of modern content-adaptive algorithms with respect to the most powerful detectors. Multiple artificial image datasets are crafted with different levels of content complexity and noise power to assess their influence on the gap between both types of detectors. Experiments with SRNet as the heuristic detector indicate that independent noise contributes less to the performance gap than content of the same MSE. While this loss is rather small for smooth images, it can be quite large for textured images. A network trained on many realizations of a fixed textured scene will, however, recuperate most of the loss, suggesting that networks have the capacity to approximately learn the parameters of a cover source narrowed to a fixed scene. 

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4. Limits of Data Driven Steganography Detectors

   https://doi.org/10.1145/3577163.3595094  

   Kaziakhmedov, E.; Dworetzky, E.; Fridrich, J. (January 2023, ACM Information Hiding and Multimedia Security Workshop)

   While deep learning has revolutionized image steganalysis in terms of performance, little is known about how much modern data-driven detectors can still be improved. In this paper, we approach this difficult and currently wide open question by working with artificial but realistic looking images with a known statistical model that allows us to compute the detectability of modern content-adaptive algorithms with respect to the most powerful detectors. Multiple artificial image datasets are crafted with different levels of content complexity and noise power to assess their influence on the gap between both types of detectors. Experiments with SRNet as the heuristic detector indicate that in dependent noise contributes less to the performance gap than content of the same MSE. While this loss is rather small for smooth images, it can be quite large for textured images. A network trained on many realizations of a fixed textured scene will, however, recuperate most of the loss, suggesting that networks have the capacity to approximately learn the parameters of a cover source narrowed to a fixed scene. 

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5. Data-driven models of nonautonomous systems

   https://doi.org/10.1016/j.jcp.2024.112976  

   Lu, H; Tartakovsky, D M (June 2024, Journal of Computational Physics)