on May 17, 2017
Overview: A number of modern applications of sequential decision making require developing strategies that are adaptive to the underlying structure of the data (graph, network, etc) and robust to change of the signal. This includes recommender systems incorporating social network information, cognitive radios, decentralized decision making, or robust structured reinforcement learning, to cite a few. In order to anticipate and impact the next generation of applications of the field, we thus need to push theory of sequential decision making to the next level, including recent development of spectral methods, low-rank matrices, and graph-based decision making. We intend to build visibility on this cross-disciplinary topic thanks to the organization of this seminar open to researchers from the field, from related fields as well as young researchers and PhD students.
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