Interpreting the structure and predictions of complex models
The workshop is in the AC Hotel right next to the conference center. Simply walk out the conference doors and turn left twice to get to the hotel. Our workshop is on the 4th floor.
Complex machine learning models, such as deep neural networks, have recently achieved great predictive successes for visual object recognition, speech perception, language modeling, and information retrieval. These predictive successes are enabled by automatically learning expressive features from the data. Typically, these learned features are a priori unknown, difficult to engineer by hand, and hard to interpret. This workshop is about interpreting the structure and predictions of these complex models.
Interpreting the learned features and the outputs of complex systems allows us to more fundamentally understand our data and predictions, and to build more effective models. For example, we may build a complex model to predict long range crime activity. But by interpreting the learned structure of the model, we can gain new insights into the processes driving crime events, enabling us to develop more effective public policy. Moreover, if we learn, for example, that the model is making good predictions by discovering how the geometry of clusters of crime events affect future activity, we can use this knowledge to design even more successful predictive models.
This 1 day workshop is focused on interpretable methods for machine learning, with an emphasis on the ability to learn structure which provides new fundamental insights into the data, in addition to accurate predictions. We wish to carefully review and enumerate modern approaches to the challenges of interpretability, share insights into the underlying properties of popular machine learning algorithms, and discuss future directions.
Note the new date: Friday, December 9