CDC 2022 (Talk)
I presented our work in collaboration with Lockheed Martin at CDC, 2022 at Cancun, Mexico on inverse-inverse reinforcement learning. Here are the slides: CDC 2022 Slides Journal version coming soon!
Revealed Preference with Information Asymmetry
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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Inverse-Inverse Reinforcement Learning
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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JMLR Paper Acceptance (with minor revision)
Our work “Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems” got accepted with minor revision at Journal of Machine Learning Research (JMLR).
CDC 2022 (Paper Acceptance)
Our paper titled “Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner” got accepted to IEEE Conference on Decision and Control (CDC), 2022!
Unifying Consumer and Information Economics
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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