Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning

Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We propose a new active learning algorithm that balances such exploration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data sets that do not. Our algorithm also shows significant tolerance of noise.

Book Review of Archaeological Theory and the Politics of Cultural Heritage by Laurajane Smith

The passage of the Native American Graves Protection and Repatriation Act (NAGPRA) in 1991 significantly changed the way archaeology would be done in the United States. This act was presaged by growing complaints and resentment directed at the scientific community by Native Americans over the treatment of their ancestral remains. Many of the underlying issues came to a head with the discovery and subsequent court battles over the 9,200-year-old individual commonly known as Kennewick Man. This had a galvanizing effect on the discipline, not only perpetuating the sometimes adversarial relationship between archaeologists and Native Americans, but also creating a rift between those archaeologists who understood Native American concerns and those who saw their ancestral skeletal remains representing the legacy of humankind and thus belonging to everyone. Similar scenarios have emerged in Australia.