skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 2

Search for: All records

Award ID contains: 2211718

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; (, Advances in Neural Information Processing Systems)
    Free, publicly-accessible full text available December 16, 2025
  2. ; (, Advances in Neural Information Processing Systems)
    Free, publicly-accessible full text available December 16, 2025
  3. ; ; ; ; ; (, Advances in Neural Information Processing Systems)
    Free, publicly-accessible full text available December 16, 2025
  4. ; (, International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  5. ; (, International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  6. ; ; (, International Conference on Learning Representations)
    Accepted Manuscript Full Text Available
  7. ; ; (, Advances in neural information processing systems (NeurIPS))
    We introduce a technique for tuning the learning rate scale factor of any base optimization algorithm and schedule automatically, which we call Mechanic. Our method provides a practical realization of recent theoretical reductions for accomplishing a similar goal in online convex optimization. We rigorously evaluate Mechanic on a range of large scale deep learning tasks with varying batch sizes, schedules, and base optimization algorithms. These experiments demonstrate that depending on the problem, Mechanic either comes very close to, matches or even improves upon manual tuning of learning rates. 
    more » « less
    Accepted Manuscript Full Text Available
  8. ; ; ; (, Proceedings of Machine Learning Research)
    We study variants of the online linear optimization (OLO) problem with bandit feedback, where the algorithm has access to external information about the unknown cost vector. Our motivation is the recent body of work on using such “hints” towards improving regret bounds for OLO problems in the full-information setting. Unlike in the full-information OLO setting, with bandit feedback, we first show that one cannot improve the standard regret bounds of O(\sqrt{T}) by using hints, even if they are always well-correlated with the cost vector. In contrast, if the algorithm is empowered to issue queries and if all the responses are correct, then we show O(\log(T)) regret is achievable. We then show how to make this result more robust — when some of the query responses can be adversarial — by using a little feedback on the quality of the responses. 
    more » « less
  9. ; (, Proceedings of Machine Learning Research)
  10. ; ; (, Proceedings of Machine Learning Research)