STOC'13 Workshop on New (Theoretical) Challenges in Machine Learning
STOC'13 Workshop on New (Theoretical) Challenges in Machine Learning

June 1, 2013

Sheraton Palo Alto Hotel
625 El Camino Real
Palo Alto, CA

Recent years have seen an explosion of interest in Machine Learning, driven in part by the use of ML technology in a wide variety of applications from natural language processing to computer vision, including in industry. Theoretical advances have had a significant positive impact on the past development of the field. Theoretically inspired techniques such as Boosting, SVM, Winnow, and others are widely used, and frameworks and analytical tools developed by the theory community are often used to guide the design of new algorithms. Yet many exciting new application settings and methods being currently developed in the field do not fit standard theoretical models. The goal of this workshop is to bring together theoretically-inclined researchers from the NIPS/ICML community with machine-learning-inclined researchers from the FOCS/STOC community to discuss these new directions and theoretical challenges. These include settings such as deep learning, structure learning, learning from new kinds of feedback and interaction, and more. The hope is to identify questions and directions where theoretical contributions can be especially impactful.


(Each talk with be 30 minutes, followed by 15 minutes for discussion.)

9:00-9:15 Opening remarks

9:15-10:00 Emmanuel Candes, Robust Subspace Clustering

10:00-10:30 Coffee break

10:30-11:15 Maria-Florina Balcan, Interactive Machine Learning

11:15-12:00 Samy Bengio, Laconic: Label Consistency for Image Categorization

12:00-2:00 lunch

2:00-2:45 Quoc Le, New developments in deep learning

2:45-3:30 Sanjoy Dasgupta, Parametrizing the Easiness of Machine Learning Problems

Organizers: Avrim Blum and Phil Long.