Machine Learning (Theory)

12/24/2009

Top graduates this season

Tags: Graduates,Machine Learning jl@ 4:55 pm

I would like to point out 3 graduates this season as having my confidence they are capable of doing great things.

  1. Daniel Hsu has diverse papers with diverse coauthors on {active learning, mulitlabeling, temporal learning, …} each covering new algorithms and methods of analysis. He is also a capable programmer, having helped me with some nitty-gritty details of cluster parallel Vowpal Wabbit this summer. He has an excellent tendency to just get things done.
  2. Nicolas Lambert doesn’t nominally work in machine learning, but I’ve found his work in elicitation relevant nevertheless. In essence, elicitable properties are closely related to learnable properties, and the elicitation complexity is related to a notion of learning complexity. See the Surrogate regret bounds paper for some related discussion. Few people successfully work at such a general level that it crosses fields, but he’s one of them.
  3. Yisong Yue is deeply focused on interactive learning, which he has attacked at all levels: theory, algorithm adaptation, programming, and popular description. I’ve seen a relentless multidimensional focus on a new real-world problem be an excellent strategy for research and expect he’ll succeed.

The obvious caveat applies—I don’t know or haven’t fully appreciated everyone’s work so I’m sure I missed people. I’d like to particularly point out Percy Liang and David Sontag as plausibly such whom I’m sure others appreciate a great deal.

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