This year’s ICML had several papers which I want to read through more carefully and understand better.

- Chun-Liang Li, Hsuan-Tien Lin, Condensed Filter Tree for Cost-Sensitive Multi-Label Classification. Several tricks accumulate to give a new approach for addressing cost sensitive multilabel classification.
- Nikos Karampatziakis and Paul Mineiro, Discriminative Features via Generalized Eigenvectors. An efficient, effective eigenvalue solution for supervised learning yields compelling nonlinear performance on several datasets.
- Nir Ailon, Zohar Karnin, Thorsten Joachims, Reducing Dueling Bandits to Cardinal Bandits. An effective method for reducing dueling bandits to normal bandits that extends to contextual situations.
- Pedro Pinheiro, Ronan Collobert, Recurrent Convolutional Neural Networks for Scene Labeling. Image parsing remains a challenge, and this is plausibly a step forward.
- Cicero Dos Santos, Bianca Zadrozny, Learning Character-level Representations for Part-of-Speech Tagging. Word morphology is clearly useful information, and yet almost all ML-for-NLP applications ignore it or hard-code it (by stemming).
- Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, Robert Schapire, Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. Statistically efficient interactive learning is now computationally feasible. I wish this one had been done in time for the NIPS tutorial ðŸ™‚
- David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin Riedmiller, Deterministic Policy Gradient Algorithms. A reduction in variance from working out the deterministic limit of policy gradient make policy gradient approaches look much more attractive.

Edit: added one that I forgot.

Aren’t you an author of paper #5?

Yep.

Don’t underestimate the joys of reading through your own paper more carefully and understanding it better.