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The class of strongly quasiconvex functions was introduced in the famous paper of B.T. Polyak in 1966. It is the natural extension of the class of strongly convex functions, its applications emcompass...
Nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years. In this paper, we propose a primal dual alternating proximal grad...
Submodularity is a fundamental phenomenon in combinatorial optimization. Submodular functions occur in a variety of combinatorial settings such as coverage problems, cut problems, welfare maximization...
Nonlinear partial differential equations (PDEs) are crucial to modelling important problems in science but they are computationally expensive and suffer from the curse of dimensionality. Since quantum...
Over the last decade, the computing needs of modern enterprise and industry have expanded to include machine intelligence, cloud computing, and HPC. In the next decade, these communities will rely on...
This symposium focuses on research topics related to efficient algorithms and data structures for discrete problems. In addition to the design of such methods and structures, the scope also includes t...
The First Symposium on Simplicity in Algorithms will be co-located with The 29th ACM-SIAM Symposium on Discrete Algorithms (SODA 2018) in New Orleans, LA, USA, January 7–10, 2018.Symposium on Simplic...
Scalable parallel algorithms for partial differential equations (PDEs) play a key role in computational science and engineering. As the number of processor cores in today's supercomputers grows rapidl...
We derive fast algorithms for doing signal reconstruction without phase. This type of problem is important insignal processing, especially speech recognition technology, and has relevance for state to...
The irreducible admissible representations of a real reductive group such as GL(n, R) have been classified by work of Langlands, Knapp, Zuckerman and Vogan. This classification is somewhat involved an...
We consider a class of nonlinear mappings FA;N in RN indexed by symmetric random matrices A 2 RNN with independent entries. Within spin glass theory, special cases of these mappings correspond to ite...
Fisher-consistent loss functions play a fundamental role in the construction of successful binary margin-based classifiers. In this paper we establish the Fisher-consistency condition for multicategor...
We present a statistical perspective on boosting. Special emphasis is given to estimating potentially complex parametric or nonparametric models, including generalized linear and additive models as we...
We congratulate the authors (hereafter BH) for an interesting take on the boosting technology, and for developing a modular computational environment in R for exploring their models. Their use of low-...
We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we provide a simple and...

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