Machine Learning (Theory)


ICML papers

Tags: Machine Learning,Papers jl@ 7:25 am

Here are some ICML papers which interested me.

  1. Arindam Banerjee had a paper which notes that PAC-Bayes bounds, a core theorem in online learning, and the optimality of Bayesian learning statements share a core inequality in their proof.
  2. Pieter Abbeel, Morgan Quigley and Andrew Y. Ng have a paper discussing RL techniques for learning given a bad (but not too bad) model of the world.
  3. Nina Balcan and Avrim Blum have a paper which discusses how to learn given a similarity function rather than a kernel. A similarity function requires less structure than a kernel, implying that a learning algorithm using a similarity function might be applied in situations where no effective kernel is evident.
  4. Nathan Ratliff, Drew Bagnell, and Marty Zinkevich have a paper describing an algorithm which attempts to fuse A* path planning with learning of transition costs based on human demonstration.

Papers (2), (3), and (4), all seem like an initial pass at solving interesting problems which push the domain in which learning is applicable.

I’d like to encourage discussion of what papers interested you and why. Maybe we’ll all learn a little bit, and it’s very likely that we all missed interesting papers in a multitrack conference.

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