Machine Learning (Theory)


Maximum Margin Mismatch?

John makes a fascinating point about structured classification (and slightly scooped my post!). Maximum Margin Markov Networks (M3N) are an interesting example of the second class of structured classifiers (where the classification of one label depends on the others), and one of my favorite papers. I’m not alone: the paper won the best student paper award at NIPS in 2003.

There are some things I find odd about the paper. For instance, it says of probabilistic models

“cannot handle high dimensional feature spaces and lack strong theoretical guarrantees.”

I’m aware of no such limitations. Also:

“Unfortunately, even probabilistic graphical models that are trained discriminatively do not achieve the same level of performance as SVMs, especially when kernel features are used.”

This is quite interesting and contradicts my own experience as well as that of a number of people I greatly
respect. I wonder what the root cause is: perhaps there is something different about the data Ben+Carlos were working with?

The elegance of M3N, I think, is unrelated to this probabilistic/margin distinction. M3N provided the first implementation of the margin concept that was computationally efficient for multiple output variables and provided a sample complexity result with a much weaker dependence than previous approaches. Further, the authors carry out some nice experiments that speak well for the practicality of their approach. In particular, M3N’s outperform Conditional Random Fields (CRFs) in terms of per-variable (Hamming) loss. And I think this gets us to the crux of the matter, and ties back to John’s post. CRFs are trained by a MAP approach that is effectively per sequence, while the loss function at run time we care about is per variable.

The mismatch the post title refers to is that, at test time, M3N’s are viterbi decoded: a per sequence decoding. Intuitively, viterbi is an algorithm that only gets paid for its services when it classifies an entire sequence correctly. This seems an odd mismatch, and makes one wonder: How well does a per-variable approach like the variable marginal likelihood approach mentioned previously of Roweis,Kakade, and Teh combined with runtime belief propagation compare with the M3N procedure? Does the mismatch matter, and if so, is there a more appropriate decoding procedure like BP, appropriate for margin-trained methods? And finally, it seems we need to answer John’s question convincingly: if you really care about per-variable probabilities or classifications, isn’t it possible that structuring the output space actually hurts? (It seems clear to me that it can help when you insist on getting the entire sequence right, although perhaps others don’t concur with that.)


What is the right form of modularity in structured prediction?

Tags: Problems,Reductions,structured jl@ 10:57 pm

Suppose you are given a sequence of observations x1,…,xT from some space and wish to predict a sequence of labels y1,…,yT so as to minimize the Hamming loss: sumi=1 to T I(yi != c(x1,…,xT)i) where c(x1,…,xT)i is the ith predicted component. For simplicity, suppose each label yi is in {0,1}.

We can optimize the Hamming loss by simply optimizing the error rate in predicting each individual component yi independently since the loss is a linear combination of losses on each individual component i. From a learning reductions viewpoint, we can learn a different classifier for each individual component. An average error rate of e over these classifiers implies an expected Hamming loss of Te. This breakup into T different prediction problems is not the standard form of modularity in structured prediction.

A more typical form of modularity is to predict yi given xi, yi-1, yi+1 where the circularity (predicting given other predictions) is handled in various ways. This is often represented with a graphical model like so:

This form of modularity seems to be preferred for several reasons:

  1. Graphical models of this sort are a natural language for expressing what we know (or believe we know) about a problem in advance.
  2. There may be computational advantages to learning to predict from fewer features. (But note that handling the circularity is sometimes computationally difficult.)
  3. There may be sample complexity advantages to learning to predict from fewer features. This is particularly true for many common learning algorithms.

The difficulty with this approach is that “errors accumulate”. In particular, an average error rate of e for each of the predictors can easily imply a hamming loss of O(eT2). Matti Kaariainen convinced me this is not improvable for predictors of this form.

So, we have two forms of modularity. One is driven by the loss function while the other driven by simplicity of prediction descriptions. Each has advantages and disadvantages from a practical viewpoint. Can these different approaches be reconciled? Is there a compelling algorithm for solving structured prediction which incorporated both intuitions?

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