Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity

  title={Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity},
  author={Sham M. Kakade and Ohad Shamir and Karthik Sridharan and Ambuj Tewari},
The versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these models in high-dimensions when the optimal parameter vector is sparse. This work characterizes a certain strong convexity property of general exponential families, which allows their generalization ability to be quantified. In particular, we show how this property can be used to analyze generic exponential families under… CONTINUE READING
Highly Cited
This paper has 56 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 4 times over the past 90 days. VIEW TWEETS

From This Paper

Topics from this paper.
38 Citations
16 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 38 extracted citations

57 Citations

Citations per Year
Semantic Scholar estimates that this publication has 57 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…