NEWS    MERL researchers presenting three papers at ICML 2020

Date released: July 13, 2020

  •  NEWS    MERL researchers presenting three papers at ICML 2020
  • Date:

    July 12, 2020 - July 18, 2020

  • Where:

    Vienna, Austria (virtual this year)

  • Description:

    MERL researchers are presenting three papers at the International Conference on Machine Learning (ICML 2020), which is virtually held this year from 12-18th July. ICML is one of the top-tier conferences in machine learning with an acceptance rate of 22%. The MERL papers are:

    1) "Finite-time convergence in Continuous-Time Optimization" by Orlando Romero and Mouhacine Benosman.

    2) "Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?" by Kei Ota, Tomoaki Oiki, Devesh Jha, Toshisada Mariyama, and Daniel Nikovski.

    3) "Representation Learning Using Adversarially-Contrastive Optimal Transport" by Anoop Cherian and Shuchin Aeron.

  • MERL Contacts:
  • Research Areas:

    Artificial Intelligence, Computer Vision, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics

    •  Romero, O., Benosman, M., "Finite-Time Convergence in Continuous-Time Optimization", International Conference on Machine Learning (ICML), July 2020.
      BibTeX TR2020-100 PDF
      • @inproceedings{Romero2020jul,
      • author = {Romero, Orlando and Benosman, Mouhacine},
      • title = {Finite-Time Convergence in Continuous-Time Optimization},
      • booktitle = {International Conference on Machine Learning},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-100}
      • }
    •  Cherian, A., Aeron, S., "Representation Learning via Adversarially-Contrastive Optimal Transport", International Conference on Machine Learning (ICML), Daumé, H. and Singh, A., Eds., July 2020, pp. 10675-10685.
      BibTeX TR2020-093 PDF Software
      • @inproceedings{Cherian2020jul,
      • author = {Cherian, Anoop and Aeron, Shuchin},
      • title = {Representation Learning via Adversarially-Contrastive Optimal Transport},
      • booktitle = {International Conference on Machine Learning (ICML)},
      • year = 2020,
      • editor = {Daumé, H. and Singh, A.},
      • pages = {10675--10685},
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-093}
      • }
    •  Ota, K., Oiki, T., Jha, D.K., Mariyama, T., Nikovski, D.N., "Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?", International Conference on Machine Learning (ICML), Daumé III , Hal and Singh, Aarti, Eds., June 2020, pp. 7424-7433.
      BibTeX TR2020-083 PDF Software
      • @inproceedings{Ota2020jun,
      • author = {Ota, Kei and Oiki, Tomoaki and Jha, Devesh K. and Mariyama, Toshisada and Nikovski, Daniel N.},
      • title = {Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?},
      • booktitle = {International Conference on Machine Learning (ICML)},
      • year = 2020,
      • editor = {Daumé III , Hal and Singh, Aarti},
      • pages = {7424--7433},
      • month = jun,
      • publisher = {PMLR},
      • url = {https://www.merl.com/publications/TR2020-083}
      • }