Applications of strong convexity--strong smoothness duality to learning with matrices

@article{Kakade2009ApplicationsOS,
  title={Applications of strong convexity--strong smoothness duality to learning with matrices},
  author={Sham M. Kakade and Shai Shalev-Shwartz and Ambuj Tewari},
  journal={CoRR},
  year={2009},
  volume={abs/0910.0610}
}
It is known that a function is strongly convex with respect to some norm if and only if its conjugate function is strongly smooth with respect to the dual norm. This result has already been found to be a key component in deriving and analyzing several learning algorithms. Utilizing this du-ality, we isolate a single inequality which seamlessly implies both generalization bounds and on-line regret bounds; and we show how to construct strongly convex functions over matrices based on strongly… CONTINUE READING
Highly Cited
This paper has 19 citations. REVIEW CITATIONS

From This Paper

Topics from this paper.
13 Citations
3 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 13 extracted citations

Similar Papers

Loading similar papers…