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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 Learning” (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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2018 Poster: Exponentiated Strongly Rayleigh Distributions »
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2018 Poster: ATOMO: Communicationefficient Learning via Atomic Sparsification »
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2017 Workshop: OPT 2017: Optimization for Machine Learning »
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2017 Poster: The Numerics of GANs »
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2017 Poster: Elementary Symmetric Polynomials for Optimal Experimental Design »
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2017 Poster: kSupport and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms »
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2017 Poster: Stabilizing Training of Generative Adversarial Networks through Regularization »
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2017 Poster: Polynomial time algorithms for dual volume sampling »
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2016 Workshop: OPT 2016: Optimization for Machine Learning »
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2016 Poster: Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling »
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2016 Poster: Kronecker Determinantal Point Processes »
Zelda Mariet · Suvrit Sra 
2016 Poster: fGAN: Training Generative Neural Samplers using Variational Divergence Minimization »
Sebastian Nowozin · Botond Cseke · Ryota Tomioka 
2016 Poster: Proximal Stochastic Methods for Nonsmooth Nonconvex FiniteSum Optimization »
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2016 Poster: Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds »
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2016 Poster: DISCO Nets : DISsimilarity COefficients Networks »
Diane Bouchacourt · Pawan K Mudigonda · Sebastian Nowozin 
2016 Tutorial: LargeScale Optimization: Beyond Stochastic Gradient Descent and Convexity »
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2015 Workshop: Optimization for Machine Learning (OPT2015) »
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2015 Poster: Matrix Manifold Optimization for Gaussian Mixtures »
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2015 Poster: On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants »
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2014 Workshop: Discrete Optimization in Machine Learning »
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2014 Workshop: OPT2014: Optimization for Machine Learning »
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2014 Poster: Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems »
Cong Han Lim · Stephen Wright 
2014 Poster: Efficient Structured Matrix Rank Minimization »
Adams Wei Yu · Wanli Ma · Yaoliang Yu · Jaime Carbonell · Suvrit Sra 
2013 Workshop: OPT2013: Optimization for Machine Learning »
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2013 Poster: Decision Jungles: Compact and Rich Models for Classification »
Jamie Shotton · Toby Sharp · Pushmeet Kohli · Sebastian Nowozin · John Winn · Antonio Criminisi 
2013 Poster: Geometric optimisation on positive definite matrices for elliptically contoured distributions »
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2013 Poster: Reflection methods for userfriendly submodular optimization »
Stefanie Jegelka · Francis Bach · Suvrit Sra 
2013 Poster: An Approximate, Efficient LP Solver for LP Rounding »
Srikrishna Sridhar · Stephen Wright · Christopher Re · Ji Liu · Victor Bittorf · Ce Zhang 
2012 Workshop: LogLinear Models »
Dimitri Kanevsky · Tony Jebara · Li Deng · Stephen Wright · Georg Heigold · Avishy Carmi 
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2012 Poster: A new metric on the manifold of kernel matrices with application to matrix geometric means »
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2012 Poster: Scalable nonconvex inexact proximal splitting »
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2011 Poster: Hogwild!: A LockFree Approach to Parallelizing Stochastic Gradient Descent »
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2011 Poster: HigherOrder Correlation Clustering for Image Segmentation »
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2010 Workshop: Numerical Mathematics Challenges in Machine Learning »
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2010 Tutorial: Optimization Algorithms in Machine Learning »
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