<html><body><div style="font-family: arial,helvetica,sans-serif; font-size: 12pt; color: #000000"><div data-marker="__QUOTED_TEXT__">Colloquium Speaker <br>Manfred K Warmuth, University of California, Santa Cruz <br>Monday, May 22, 12:30pm <br>Computer Science 105<br><br>The blessing and the curse of the multiplicative updates - discusses connections between in evolution and the multiplicative updates of online learning<br><br><br>Multiplicative updates multiply the parameters by nonnegative factors. These updates are motivated by a Maximum Entropy Principle and they are prevalent in evolutionary processes where the parameters are for example concentrations of species and the factors are survival rates. The simplest such update is Bayes rule and we give an in vitro selection algorithm for RNA strands that implements this rule in the test tube where each RNA strand represents a different model. In one liter of the RNA soup there are approximately 10^15 different strands and therefore this is a rather high-dimensional implementation of Bayes rule.<br><br>We investigate multiplicative updates for the purpose of learning online while processing a stream of examples. The ``blessing'' of these updates is that they learn very fast in the short term because the good parameters grow exponentially. However their ``curse'' is that they learn too fast and wipe out parameters too quickly. This can have a negative effect in the long term. We describe a number of methods developed in the realm of online learning that ameliorate the curse of the multiplicative updates. The methods make the algorithm robust against data that changes over time and prevent the currently good parameters from taking over. We also discuss how the curse is circumvented by nature. Surprisingly, some of nature's methods parallel the ones developed in Machine Learning, but nature also has some additional tricks.<br><br>This will be a high level talk.<br>No background in online learning will be required.<br>We will give a number of open problems and discuss how these updates are applied for training feed forward neural nets.<br></div></div></body></html>