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Optimization for Machine Learning

Our workshop focuses on optimization theory and practice that is relevant to machine learning. This proposal builds on precedent established by two of our previously well-received NIPS workshops:

Both these workshops had packed (often overpacked) attendance almost throughout the day. This enthusiastic reception reflects the strong interest, relevance, and importance enjoyed by optimization in the greater ML community. One could ask why does optimization attract such continued interest? The answer is simple but telling: optimization lies at the heart of almost every ML algorithm. For some algorithms textbook methods suffice, but the majority require tailoring algorithmic tools from optimization, which in turn depends on a deeper understanding of the ML requirements. In fact, ML applications and researchers are driving some of the most cuttingedge developments in optimization today. The intimate relation of optimization with ML is the key motivation for our workshop, which aims to foster discussion, discovery, and dissemination of the state-of-the-art in optimization, especially in the context of ML. The workshop should realize its aims by:

  • Providing a platform for increasing the interaction between researchers from optimization, operations research, statistics, scientific computing, and machine learning;
  • Identifying key problems and challenges that lie at the intersection of optimization and ML;
  • Narrowing the gap between optimization and ML, to help reduce rediscovery, and thereby accelerating new advances.

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