Discrete optimization problems and combinatorial structures are becoming increasingly important in machine learning. In the endeavour to process more and more complex data, one may quickly find oneself working with graphs, relations, partitions, or sparsity structure. In addition, we may want to predict structured, sparse estimators, or combinatorial objects such as permutations, trees or other graphs, group structure and so on.
While complex structures enable much richer applications, they often come with the caveat that the related learning and inference problems become computationally very hard. When scaling to large data, combinatorial problems also add some further challenges to compact representations, streaming and distributed computation.
Despite discouraging theoretical worst-case results, many practically interesting problems can be much more well behaved (when modeled appropriately), or are well modeled by assuming properties that make them so. Such properties are most often structural and include symmetry, exchangeability, sparsity, or submodularity. Still, not all of these structures and their application ranges are well understood. The DISCML workshop revolves around such structures in machine learning, their applications and theory.
- Andreas Krause, ETH Zurich
- Jeff A. Bilmes, University of Washington
- Stefanie Jegelka, UC Berkeley