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NEGDEPML 2019 : ICML Workshop on Negative Dependence in ML


When Jun 14, 2019 - Jun 15, 2019
Where Long Beach, California
Submission Deadline Apr 29, 2019
Categories    machine learning   optimization

Call For Papers

Whether selecting training data, finding an optimal experimental design, exploring in reinforcement learning, or designing recommender systems, selecting a high-quality but diverse set of items is a core challenge for ML.

Any task that requires selecting multiple, non-similar items leverages the concept of negative dependence. Negatively-dependent measures and submodularity are powerful, theoretically-grounded tools that can aid in this selection.

Determinantal point processes are arguably the most popular negatively-dependent measure, with past applications including recommender systems, neural network pruning, ensemble learning, summarization, and kernel reconstruction. However, the spectrum of negatively-dependent measures is much broader.

This workshop will discuss with the ICML audience the rich mathematical tools associated with negative dependence, delving into the key theoretical concepts that underlie negatively-dependent measures and investigating fundamental applications.

We invite submissions of papers on any topic related to negative dependence in machine learning, including (but not limited to):
- Submodular optimization
- Determinantal point processes
- Volume sampling
- Recommender systems
- Experimental design
- Variance-reduction methods
- Exploitation/exploration trade-offs (RL, Bayesian Optimization, etc.)
- Batched active learning
- Strongly Rayleigh measures

- Mike Gartrell (Criteo AI Lab)
- Jennifer Gillenwater (Google Research NY)
- Alex Kulesza (Google Research NY)
- Zelda Mariet (MIT)

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