ICML Workshop on Negative Dependence in ML
NEGDEPML 2019
- URL: https://negative-dependence-in-ml-workshop.lids.mit.edu/
- Event Date: 2019-06-14 ~ 2019-06-15
- Submission Date: 2019-04-29
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.
SUBMISSIONS
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
ORGANIZERS
- Mike Gartrell (Criteo AI Lab)
- Jennifer Gillenwater (Google Research NY)
- Alex Kulesza (Google Research NY)
- Zelda Mariet (MIT)
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.
SUBMISSIONS
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
ORGANIZERS
- Mike Gartrell (Criteo AI Lab)
- Jennifer Gillenwater (Google Research NY)
- Alex Kulesza (Google Research NY)
- Zelda Mariet (MIT)
Journal Promotion
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| Acta Koreana (Acta Koreana) |
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| Acta Via Serica (AVS) |
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| Computers and Concrete (CAC) |
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| Journal of Information Processing Systems (JIPS) |
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| Journal of Internet Computing and Services (KSII - JICS) |
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| Journal of Smart Tourism (Smart Tourism) |
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| Membrane and Water Treatment (MWT) |
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| Wind and Structures, An International Journal (Wind and Structures) |
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