CFP: NIPS 2011 Workshop on Discrete Optimization in Machine Learning (DISCML) – Uncertainty, Generalization and Feedback

Call for Papers

3rd Workshop on
Discrete Optimization in Machine Learning (DISCML):
Uncertainty, Generalization and Feedback

at the
25th Annual Conference on Neural Information Processing Systems
(NIPS 2011)

Submission Deadline: Friday 11/11/11, 11:11

Solving optimization problems with ultimately discretely solutions is
becoming increasingly important in machine learning: At the core of
statistical machine learning is to infer conclusions from data, and
when the variables underlying the data are discrete, both the tasks of
inferring the model from data, as well as performing predictions using
the estimated model are discrete optimization problems. This workshop
aims at exploring discrete structures relevant to machine learning and
techniques relevant to solving discrete learning problems.
The focus of this year’s workshop is on the interplay between discrete
optimization and machine learning: How can we solve inference problems
arising in machine learning using discrete optimization? How can one
solve discrete optimization problems that themselves are learned from
training data? How can we solve challenging sequential and adaptive
discrete optimization problems where we have the opportunity to
incorporate feedback? We will also explore applications of such

We would like to encourage high quality submissions of short papers
relevant to the workshop topics. Accepted papers will be presented as
spotlight talks and posters. Of particular interest are new
algorithms with theoretical guarantees, as well as applications of
discrete optimization to machine learning problems in areas such as
the following:

Combinatorial algorithms

* Submodular & supermodular optimization
* Discrete convex analysis
* Pseudo-boolean optimization
* Randomized / approximation algorithms

Continuous relaxations

* Sparse approximation & compressive sensing
* Regularization techniques
* Structured sparsity models

Learning in discrete domains

* Online learning / bandit optimization
* Generalization in discrete optimization
* Adaptive / stochastic optimization


* Graphical model inference & structure learning
* Clustering
* Feature selection, active learning & experimental design
* Structured prediction
* Novel discrete optimization problems in ML, Computer Vision, NLP, …

Submission deadline: November 11, 2011

Length & Format: max. 6 pages NIPS 2011 format

Time & Location: December 16/17 2011, Sierra Nevada, Spain

Submission instructions: Email to submit(at)

Andreas Krause (ETH Zurich, Switzerland),
Pradeep Ravikumar (University of Texas, Austin),
Jeff A. Bilmes (University of Washington),
Stefanie Jegelka (Max Planck Institute for Intelligent Systems, Germany)