Here is a set of papers that I found interesting (and why).
- A PAC-Bayes approach to the Set Covering Machine improves the set covering machine. The set covering machine approach is a new way to do classification characterized by a very close connection between theory and algorithm. At this point, the approach seems to be competing well with SVMs in about all dimensions: similar computational speed, similar accuracy, stronger learning theory guarantees, more general information source (a kernel has strictly more structure than a metric), and more sparsity. Developing a classification algorithm is not very easy, but the results so far are encouraging.
- Off-Road Obstacle Avoidance through End-to-End Learning and Learning Depth from Single Monocular Images both effectively showed that depth information can be predicted from camera images (using notably different techniques). This ability is strongly enabling because cameras are cheap, tiny, light, and potentially provider longer range distance information than the laser range finders people traditionally use.
- The Forgetron: A Kernel-Based Perceptron on a Fixed Budget proved that a bounded memory kernelized perceptron algorithm (which might be characterizable as “stochastic functional gradient descent with weight decay and truncation”) competes well with respect to an unbounded memory algorithm when the data contains a significant margin. Roughly speaking, this implies that the perceptron approach can learn arbitary (via the kernel) reasonably simple concepts from unbounded quantities of data.
(Feel free to add any that you found interesting.)