Our conference paper on inverse reinforcement learning under information asymmetry constraints in collaboration with Lockheed Martin is on arXiv! We investigate how privileged information allows simultaneous identification and mitigation of adversarial entities. We draw ...
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Our journal paper on inverse-inverse reinforcement learning (I2RL) in collaboration with Lockheed Martin is on arXiv! We investigate how a cognitive radar can mask its strategy from an adversarial eavesdropper performing inverse reinforcement learning. ...
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Our manuscript on unifying deterministic and stochastic revealed preference is on arXiv! We unify two prominent lines of work in economics, namely, revealed preference in consumer economics and identifying rational inattention in information economics. ...
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Our manuscript on interpretable AI is on arXiv! In this paper, we demonstrate that deep CNNs for image classification can be modeled (in terms of necessary and sufficient conditions) to rationally inattentive utility maximizers, ...
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