It is fair to say that at the heart of every machine learning algorithm is an optimization problem. It is only recently that this viewpoint has gained significant following. Classical optimization techniques based on convex optimization have occupied centerstage due to their attractive theoretical properties. But, new nonsmooth and nonconvex problems are being posed by machine learning paradigms such as structured learning and semisupervised learning. Moreover, machine learning is now very important for realworld problems which often have massive datasets, streaming inputs, and complex models that also pose significant algorithmic and engineering challenges. In summary, machine learning not only provides interesting applications but also challenges the underlying assumptions of most existing optimization algorithms.
Therefore, there is a pressing need for optimization "tuned" to the machine learning context. For example, techniques such as nonconvex optimization (for semisupervised learning), combinatorial optimization and relaxations (structured learning), nonsmooth optimization (sparsity constraints, L1, Lasso, structure learning), stochastic optimization (massive datasets, noisy data), decomposition techniques (parallel and distributed computation), and online learning (streaming inputs) are relevant in this setting. These techniques naturally draw inspiration from other fields, such as operations research, theoretical computer science, and the optimization community.
Motivated by these concerns, we would like to address these issues in the framework of this workshop.
Author Information
Sebastian Nowozin (Microsoft Research)
Suvrit Sra (MIT)
Suvrit Sra is a faculty member within the EECS department at MIT, where he is also a core faculty member of IDSS, LIDS, MITML Group, as well as the statistics and data science center. His research spans topics in optimization, matrix theory, differential geometry, and probability theory, which he connects with machine learning  a key focus of his research is on the theme "Optimization for Machine Learningâ€ť (http://optml.org)
S.V.N Vishwanthan (Purdue University)
Stephen Wright (UWMadison)
Steve Wright is a Professor of Computer Sciences at the University of WisconsinMadison. His research interests lie in computational optimization and its applications to science and engineering. Prior to joining UWMadison in 2001, Wright was a Senior Computer Scientist (19972001) and Computer Scientist (19901997) at Argonne National Laboratory, and Professor of Computer Science at the University of Chicago (20002001). He is the past Chair of the Mathematical Optimization Society (formerly the Mathematical Programming Society), the leading professional society in optimization, and a member of the Board of the Society for Industrial and Applied Mathematics (SIAM). Wright is the author or coauthor of four widely used books in numerical optimization, including "Primal Dual InteriorPoint Methods" (SIAM, 1997) and "Numerical Optimization" (with J. Nocedal, Second Edition, Springer, 2006). He has also authored over 85 refereed journal papers on optimization theory, algorithms, software, and applications. He is coauthor of widely used interiorpoint software for linear and quadratic optimization. His recent research includes algorithms, applications, and theory for sparse optimization (including applications in compressed sensing and machine learning).
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