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Workshop DescriptionWhatThe workshop proposes to focus on relations between machine learning problems. We use “relation” quite generally to include (but not limit ourselves to) notions such as:
WhyThe point of studying relations between machine learning problems is that it stands a reasonable chance of being a way to be able to understand the field of machine learning as a whole. It could serve to prevent reinvention, and rapidly facilitate the growth of new methods. The motivation is not dissimilar to Hal Varian’s notion of combinatorial innovation. Another analogy is to consider the development of function theory in the 19th century and observe the rapid advances made possible by the development of functional analysis, which, rather than studying individual functions, studied operators that transformed one function to another.Much recent work in machine learning can be interpreted as relations between problems. For example:
(If the
reader is unconvinced, consider the following partial list: batch,
online, transductive, offtraining set, semisupervised, noisy (label,
attribute, constant noise / variable noise, data of variable quality),
data of different costs, weighted loss functions, active, distributed,
classification (binary weighted binary multiclass), structured output,
probabilistic concepts / scoring rules, class probability estimation,
learning with statistical queries, NeymanPearson classification,
regression, ordinal regression, ranked regression, ranking, ranking the
best, optimising the ROC curve, optimising the AUC, regression,
selection, novelty detection, multiinstance learning, minimum volume
sets, density level sets, regression level sets, sets of quantiles,
quantile regression, density estimation, data segmentation, clustering,
cotraining, covalidation, learning with constraints, conditional
estimators, estimated loss, confidence / hedging estimators, hypothesis
testing, distributional distance estimation, learning relations,
learning total orders, learning causal relationships, and estimating
performance (cross validation)!
Current AttemptsThere are few current attempts to build a better understanding of machine learning via relations between problems. One attempt (by some of the organisers) is the Reconceiving Machine Learning project.Desired Outcomes
