hunch_net hunch_net-2008 hunch_net-2008-328 knowledge-graph by maker-knowledge-mining
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Introduction: Claude Sammut is attempting to put together an Encyclopedia of Machine Learning . I volunteered to write one article on Efficient RL in MDPs , which I would like to invite comment on. Is something critical missing?
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same-blog 1 1.0 328 hunch net-2008-11-26-Efficient Reinforcement Learning in MDPs
Introduction: Claude Sammut is attempting to put together an Encyclopedia of Machine Learning . I volunteered to write one article on Efficient RL in MDPs , which I would like to invite comment on. Is something critical missing?
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Introduction: Paul Mineiro has started Machined Learnings where he’s seriously attempting to do ML research in public. I personally need to read through in greater detail, as much of it is learning reduction related, trying to deal with the sorts of complex source problems that come up in practice.
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Introduction: Several people have had difficulty with comments which seem to have an allowed language significantly poorer than posts. The set of allowed html tags has been increased and the markdown filter has been put in place to try to make commenting easier. I’ll put some examples into the comments of this post.
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Introduction: …is discussed in this nytimes article . I generally expect such approaches to become more common since computers are getting faster, machine learning is getting better, and data is becoming more plentiful. This is another example where machine learning technology may have a huge economic impact. Some side notes: We-in-research know almost nothing about how these things are done (because it is typically a corporate secret). … but the limited discussion in the article seem naive from a machine learning viewpoint. The learning process used apparently often fails to take into account transaction costs. What little of the approaches is discussed appears modeling based. It seems plausible that more direct prediction methods can yield an edge. One difficulty with stock picking as a research topic is that it is inherently a zero sum game (for every winner, there is a loser). Much of the rest of research is positive sum (basically, everyone wins).
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Introduction: Ron Bekkerman initiated an effort to create an edited book on parallel machine learning that Misha and I have been helping with. The breadth of efforts to parallelize machine learning surprised me: I was only aware of a small fraction initially. This put us in a unique position, with knowledge of a wide array of different efforts, so it is natural to put together a survey tutorial on the subject of parallel learning for KDD , tomorrow. This tutorial is not limited to the book itself however, as several interesting new algorithms have come out since we started inviting chapters. This tutorial should interest anyone trying to use machine learning on significant quantities of data, anyone interested in developing algorithms for such, and of course who has bragging rights to the fastest learning algorithm on planet earth (Also note the Modeling with Hadoop tutorial just before ours which deals with one way of trying to speed up learning algorithms. We have almost no
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same-blog 1 0.94519067 328 hunch net-2008-11-26-Efficient Reinforcement Learning in MDPs
Introduction: Claude Sammut is attempting to put together an Encyclopedia of Machine Learning . I volunteered to write one article on Efficient RL in MDPs , which I would like to invite comment on. Is something critical missing?
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Introduction: Several people have had difficulty with comments which seem to have an allowed language significantly poorer than posts. The set of allowed html tags has been increased and the markdown filter has been put in place to try to make commenting easier. I’ll put some examples into the comments of this post.
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Introduction: This post is some combination of belaboring the obvious and speculating wildly about the future. The basic issue to be addressed is how to think about machine learning in terms given to us from Programming Language theory. Types and Reductions John’s research programme (I feel this should be in British spelling to reflect the grandiousness of the idea…) of machine learning reductions StateOfReduction is at some essential level type-theoretic in nature. The fundamental elements are the classifier, a function f: alpha -> beta, and the corresponding classifier trainer g: List of (alpha,beta) -> (alpha -> beta). The research goal is to create *combinators* that produce new f’s and g’s given existing ones. John (probably quite rightly) seems unwilling at the moment to commit to any notion stronger than these combinators are correctly typed. One way to see the result of a reduction is something typed like: (For those denied the joy of the Hindly-Milner type system, “simple” is probab
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Introduction: In my experience, there are two different groups of people who believe the same thing: the mathematics encountered in typical machine learning conference papers is often of questionable value. The two groups who agree on this are applied machine learning people who have given up on math, and mature theoreticians who understand the limits of theory. Partly, this is just a statement about where we are with respect to machine learning. In particular, we have no mechanism capable of generating a prescription for how to solve all learning problems. In the absence of such certainty, people try to come up with formalisms that partially describe and motivate how and why they do things. This is natural and healthy—we might hope that it will eventually lead to just such a mechanism. But, part of this is simply an emphasis on complexity over clarity. A very natural and simple theoretical statement is often obscured by complexifications. Common sources of complexification include:
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Introduction: Claude Sammut is attempting to put together an Encyclopedia of Machine Learning . I volunteered to write one article on Efficient RL in MDPs , which I would like to invite comment on. Is something critical missing?
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