- Scalable Training of L1-regularized Log-linear ModelsThe main idea is to do L-BFGS in an orthant where the gradient of the L1 loss doesn't change. Each time BFGS tries to step out of that orthant, project it's new point on the old orthant, and figure out the new orthant to explore
- Discriminative Learning for Differing Training and Test DistributionsIn addition to learning P(Y|X,t1), also learn P(this point is from test data|X). You can do logistic regression to model P(this point is from test data|X,t2), and then weight each point in training set by that value when learning P(Y|X). Alternatively you can learn both distributions simultaneously by maximizing P(Y|X,t1,t2) on test data, which gives even better results
- On One Method of Non-Diagonal Regularization in Sparse Bayesian Learning Relevance Vector Machine "fits" a diagonal Gaussian prior to data by maximizing P(data|prior).

In the paper they get a tractable method of fitting Laplace/Gaussian priors with non-diagonal matrices by first transforming parameters to a basis which uncorrelates the parameters at the point of maximum likelihood. - Piecewise Pseudolikelihood for Efficient Training of Conditional Random FieldsDoing pseudo-likelihood training (replacing p(y1,y2|x) with p(y1|y2,x)p(y2|y1,x)) on small pieces of the graph (piece-wise training) gives better accuracy than pseudo-likelihood training on the true graph
- CarpeDiem: an Algorithm for the Fast Evaluation of SSL Classifiers -- a useful trick for doing Viterbi faster -- don't bother computing forward values for nodes which are certain to not be included in the best path. You know a node will not be included in the search path if the a+b+c is smaller than some other forward value on the same level. a+b+c is largest forward value on previous level, b is largest possible transition weight, c is the "emission" weight for that node

## Thursday, June 28, 2007

### Cool Papers at ICML 07

Here are a few that caught my eye:

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