ACML 2015 - Workshop on Algorithmic Challenges in Machine Learning

With the ubiquity of big data, there has been a rise in interest in machine learning and data-based applications. This workshop aims to bring together theoretical computer scientists interested in machine learning with theoretically-inclined machine learning researchers to discuss challenges and opportunities at the intersection of these two fields. The workshop will cover a variety of topics such as spectral learning, online learning, bandits, sparsity, and unsupervised methods. Talks will consist of both survey talks and more specialized technical talks. We will also have allocated time for collaboration and informal discussions.
For further details, please contact Kamalika Chaudhuri or Shachar Lovett.
Confirmed speakers include:
Nina Balcan, Carnegie Mellon University
Peter Bartlett, Berkeley
Avrim Blum, Carnegie Mellon University
Ofer Dekel, Micorsoft Research - Redmond
Moritz Hardt, IBM Research - Almaden
Daniel Hsu, Columbia
Piotr Indyk, MIT
Ankur Moitra, MIT
Greg Valiant, Stanford
Manfred Warmuth, UC Santa Cruz
Yuval Peres, Micorsoft Research - Redmond