UW – MSR Machine Learning workshop 2015 – Session 1

9:30 Opening Remarks - Ran Gilad-Bachrach 9:45 Toward Scalable Visual Recognition - Ali Farhadi Recognition is graduating from labs to real-world applications. While it is encouraging to see its potential being tapped, it brings forth a fundamental challenge: scalability. How can we learn a model for any concept that exhaustively covers all its appearance variations, while requiring minimal or no human supervision for compiling the vocabulary of visual variance, gathering the training images and annotations, and learning the models? In this work, I will introduce a fully-automated approach for learning extensive models for a wide range of variations (e.g. actions, interactions, attributes and beyond) within any concept. I will also showcase novel applications that our fully automated system enables including scalable image segmentation and visual verification. 10:10 Advances in Anomaly Detection - Tom Dietterich Our team at Oregon State University has developed several new algorithms for anomaly detection. These are based on two main principles: "anomaly detection by underfitting" and "anomaly detection by overfitting". In the underfitting approach, a model is fit to the data and points that do not fit well (e.g., that have low estimated probability density) are flagged as anomalies. In the overfitting approach, we transform the data to create learning problems in which there should be no signal or pattern and then apply machine learning algorithms to fit this data. If the algorithm finds a pattern, this is due to overfitting, and points belonging to the (false) pattern are likely to be anomalies. This talk will present these algorithms and also describe our benchmarking framework, which allows us to measure and compare the performance of different anomaly detection algorithms. I will also describe the results of a red-team experiment conducted under the DARPA ADAMS program in which our anomaly detection methods are showing excellent performance. 10:35 Spotlight: Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization - Tyler Johnson By reducing optimization to a sequence of small subproblems, working set methods achieve some of the fastest convergence…