Saturday, September 22, 2012

[DMANET] Deadline extension: 4th NIPS workshop on Discrete Optimization in Machine Learning


4th Workshop on
Discrete Optimization in Machine Learning (DISCML):
Structure and Scalability

at the Annual Conference on Neural Information Processing Systems (NIPS 2012)


*** UPDATE: new Submission DEADLINE: October 10, 2012 ***

Optimization problems with ultimately discretely solutions are
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. Two factors
complicate matters: first, many discrete problems are in the general
case very hard, and second, machine learning applications often demand
solving such problems at large scale. The focus of this year's
workshop lies on structures that enable scalability. Which properties
of the problem make it possible to still efficiently obtain exact or
decent approximate solutions? What are the challenges posed by
parallel and distributed processing? Which discrete problems in
machine learning are in need of more scalable algorithms? How can we
make discrete algorithms scalable? Some heuristics perform well but are
as yet devoid of a theoretical foundation. What explains this

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.

Areas of interest include


• Combinatorial algorithms
• Submodular / supermodular optimization
• Discrete Convex Analysis
• Pseudo-boolean optimization
• Parallel & distributed discrete optimization

Continuous relaxations

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

Learning in discrete domains

• Online learning / bandit optimization
• Generalization in discrete learning problems
• 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: October 10, 2012

Length & Format: max. 6 pages NIPS 2012 format

Time & Location: December 7 or 8 2012, Lake Tahoe, Nevada, USA

Submission: via email to

Invited talks by

• Satoru Fujishige
• Amir Globerson
• Alex Smola

Andreas Krause (ETH Zurich, Switzerland),
Jeff A. Bilmes (University of Washington),
Pradeep Ravikumar (University of Texas, Austin),
Stefanie Jegelka (UC Berkeley)

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