[AI Seminar] AI Seminar Sponsored by Apple -- Misha Khodak, Efficient and Adaptive Meta-Learning with Provable Guarantees

[Han Zhao]

Dear faculty and students:

We look forward to seeing you on Tuesday, Dec. 3rd, at noon in *NSH 3305 *for
our AI Seminar sponsored by Apple. To learn more about the seminar series,
please visit the website <http://www.cs.cmu.edu/~aiseminar/>.
On Tuesday, Misha Khodak will give the following talk:
*Title: **Efficient and Adaptive Meta-Learning with Provable Guarantees*

*Abstract:* Meta-learning has recently re-emerged as an important direction
for developing algorithms for multi-task learning, dynamic environments,
and federated settings; however, meta-learning approaches that can scale to
deep neural networks are largely heuristic and lack formal guarantees. We
build a theoretical framework for designing and understanding practical
meta-learning methods that integrates sophisticated formalizations of
task-similarity with the extensive literature on online convex optimization
and sequential prediction algorithms. Our approach enables the
task-similarity to be learned adaptively, provides sharper transfer-risk
bounds in the setting of statistical learning-to-learn, and leads to
straightforward derivations of average-case regret bounds for efficient
algorithms in settings where the task-environment changes dynamically or
the tasks share a certain geometric structure. We use our theory to modify
several popular meta-learning algorithms and improve performance on
standard problems in few-shot and federated learning.

Joint work Nina Balcan, Ameet Talwalkar, Jeff Li, and Sebastian Caldas
-- 

*Han ZhaoMachine Learning Department*


*School of Computer ScienceCarnegie Mellon UniversityMobile: +1-*
*412-652-4404*
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/ai-seminar-announce/attachments/20191130/77ea367c/attachment.html>