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1. Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner

   https://doi.org/10.1109/CDC51059.2022.9992959  

   Pattanayak, Kunal ; Krishnamurthy, Vikram ; Berry, Christopher ( December 2022 , 2022 IEEE 61st Conference on Decision and Control (CDC))

   Inverse reinforcement learning (IRL) deals with estimating an agent’s utility function from its actions. In this paper, we consider how an agent can hide its strategy and mitigate an adversarial IRL attack; we call this inverse IRL (I-IRL). How should the decision maker choose its response to ensure a poor reconstruction of its strategy by an adversary performing IRL to estimate the agent’s strategy? This paper comprises four results: First, we present an adversarial IRL algorithm that estimates the agent’s strategy while controlling the agent’s utility function. Then, we propose an I-IRL result that mitigates the IRL algorithm used by the adversary. Our I-IRL results are based on revealed preference theory in microeconomics. The key idea is for the agent to deliberately choose sub-optimal responses so that its true strategy is sufficiently masked. Third, we give a sample complexity result for our main I-IRL result when the agent has noisy estimates of the adversary-specified utility function. Finally, we illustrate our I-IRL scheme in a radar problem where a meta-cognitive radar is trying to mitigate an adversarial target. 

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2. High-Frequency Limit of the Inverse Scattering Problem: Asymptotic Convergence from Inverse Helmholtz to Inverse Liouville

   https://doi.org/10.1137/22M147075X  

   Chen, Shi ; Ding, Zhiyan ; Li, Qin ; Zepeda-Núñez, Leonardo ( March 2023 , SIAM Journal on Imaging Sciences)
3. A-optimal encoding weights for nonlinear inverse problems, with application to the Helmholtz inverse problem

   https://doi.org/10.1088/1361-6420/aa6d8e  

   Crestel, Benjamin ; Alexanderian, Alen ; Stadler, Georg ; Ghattas, Omar ( July 2017 , Inverse Problems)
4. Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling [Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling]

   https://doi.org/10.5220/0009156201850195  

   Olmschenk, Greg ; Tang, Hao ; Zhu, Zhigang ( January 2020 , 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications)

   Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks is less effective than our alternative inverse k-nearest neighbor (i$k$NN) maps, even when used directly in existing state-of-the-art network structures. We also provide a new network architecture MUD-i$k$NN, which uses multi-scale upsampling via transposed convolutions to take full advantage of the provided i$k$NN labeling. This upsampling combined with the i$k$NN maps further improves crowd counting accuracy. Our new network architecture performs favorably in comparison with the state-of-the-art. However, our labeling and upsampling techniques are generally applicable to existing crowd counting architectures. 

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5. Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations

   Guangmo Tong ( December 2022 , Advances in Neural Information Processing Systems 35 (NeurIPS 2022))