Reminder: CFC NIPS Workshop on Discrete Optimization in Machine Learning (DISCML)


Call for Contributions

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

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

Submission Deadline: Sunday 16th September, 11:59pm Samoa time

– We apologize for multiple postings –

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 disrete algorithms scalable? Some heuristics perform well but are as yet devoid of a theoretical foundation. What explains this behaviour?

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: September 16, 2012

Length & Format: max. 6 pages NIPS 2012 format

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

Submission instructions: Email

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)