Maxim Raginsky

If you made any changes in Pure these will be visible here soon.

Fingerprint Fingerprint is based on mining the text of the expert's scholarly documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

  • 2 Similar Profiles

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output

  • 59 Conference contribution
  • 33 Article
  • 4 Conference article
  • 1 Review article

Approximate Nash equilibria in partially observed stochastic games with mean-field interactions

Saldi, N., Başar, T. & Raginsky, M., Jan 1 2019, In : Mathematics of Operations Research. 44, 3, p. 1006-1033 28 p.

Research output: Contribution to journalArticle

  • Linear Noisy Networks with Stochastic Components

    Sevuktekin, N. C., Raginsky, M. & Singer, A. C., Dec 2019, 2019 IEEE 58th Conference on Decision and Control, CDC 2019. Institute of Electrical and Electronics Engineers Inc., p. 5386-5391 6 p. 9029965. (Proceedings of the IEEE Conference on Decision and Control; vol. 2019-December).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Partially-Observed Discrete-Time Risk-Sensitive Mean-Field Games

    Saldi, N., Basar, T. & Raginsky, M., Dec 2019, 2019 IEEE 58th Conference on Decision and Control, CDC 2019. Institute of Electrical and Electronics Engineers Inc., p. 317-322 6 p. 9029343. (Proceedings of the IEEE Conference on Decision and Control; vol. 2019-December).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Robustness to Incorrect Models in Average-Cost Optimal Stochastic Control

    Kara, A. D., Raginsky, M. & Yuksel, S., Dec 2019, 2019 IEEE 58th Conference on Decision and Control, CDC 2019. Institute of Electrical and Electronics Engineers Inc., p. 7970-7975 6 p. 9029801. (Proceedings of the IEEE Conference on Decision and Control; vol. 2019-December).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • A machine learning methodology for inferring network S-parameters in the presence of variability

    Ma, X., Raginsky, M. & Cangellaris, A. C., Jun 29 2018, 2018 IEEE 22nd Workshop on Signal and Power Integrity, SPI 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 1-4 4 p. (2018 IEEE 22nd Workshop on Signal and Power Integrity, SPI 2018 - Proceedings).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution