Friday, Oct 20, 2017
SMI 211, 3:30PM
John Duchi, Stanford University (Departments of Statistics and Electrical Engineering)
TITLE: Composite modeling and optimization, with applications to phase retrieval and nonlinear observation modeling
ABSTRACT: We consider minimization of stochastic functionals that are compositions of a (potentially) non-smooth convex function h and smooth function c. We develop two stochastic methods–a stochastic prox-linear algorithm and a stochastic (generalized) sub-gradient procedure–and prove that, under mild technical conditions, each converges to first-order stationary points of the stochastic objective. Additionally, we analyze this problem class in the context of phase retrieval and more generic nonlinear modeling problems, showing that we can solve these problems (even with faulty measurements) with extremely high probability under appropriate random measurement models. We provide substantial experiments investigating our methods, indicating the practical effectiveness of the procedures.
BIO: John Duchi is an assistant professor of Statistics and Electrical Engineering and (by courtesy) Computer Science at Stanford University, with graduate degrees from UC Berkeleyand undergraduate degrees from Stanford. His work focuses on large scale optimization problems arising out of statistical and machine learning problems, robustness and uncertain data problems, and information theoretic aspects of statistical learning. He has won a number of awards and fellowships, including a best paper award at the International Conference onMachine Learning, an NSF CAREER award, and a Sloan Fellowship in Mathematics.
June 6, 2017, 4pm
Hongzhou Lin, Inria Grenoble
In this talk, we propose a generic approach to accelerate gradient-based optimization algorithms with quasi-Newton principles. The proposed scheme, called QuickeNing, can be applied to incremental first-order methods such as stochastic variance-reduced gradient (SVRG) or incremental surrogate optimization (MISO). It is also compatible with composite objectives, meaning that it has the ability to provide exactly sparse solutions when the objective involves a sparsity-inducing regularization. QuickeNing relies on limited-memory BFGS rules, making it appropriate for solving high-dimensional optimization problems. Besides, it enjoys a worst-case linear convergence rate for strongly convex problems. We present experimental results where QuickeNing gives significant improvements over competing methods for solving large-scale high-dimensional machine learning problems.
May 30, 2017, 4pm
Kellie MacPhee, Department of Mathematics, University of Washington
Abstract: Fenchel-Young duality is widely used in convex optimization and relies on the conjugacy operation for convex functions; however, alternative notions of duality relying on parallel operations exist as well. In particular, gauge and perspective duality are defined via the polarity operation on gauge functions. We present a perturbation argument for deriving gauge duality, thus placing it on equal footing with Fenchel-Young duality. This approach also yields explicit optimality conditions (analogous to KKT conditions), and a simple primal-from-dual recovery method based on existing algorithms. Numerical results confirm the usefulness of this approach in certain contexts (e.g. optimization over PLQ functions).
May 9, 2017, 4pm
Peng Zheng, Department of Applied Mathematics, University of Washington
Abstract: Performance of machine learning approaches is strongly influenced by choice of misfit penalty, and correct settings of penalty parameters, such as the threshold of the Huber function. These parameter are typically chosen using expert knowledge, cross-validation, or black-box optimization, which are time consuming for large-scale applications.
We present a data-driven approach that simultaneously solves inference problems and learns error structure and penalty parameters. We discuss theoretical properties of these joint problems, and present algorithms for their solution. We show numerical examples from the piecewise linear-quadratic (PLQ) family of penalties.
May 1, 2017, 4pm
Scott Roy, Department of Mathematics, University of Washington
Abstract: In a recent paper, Bubeck, Lee, and Singh introduced a new first order method for minimizing smooth strongly convex functions. Their geometric descent algorithm, largely inspired by the ellipsoid method, enjoys the optimal linear rate of convergence. We show that the same iterate sequence is generated by a scheme that in each iteration computes an optimal average of quadratic lower-models of the function. Indeed, the minimum of the averaged quadratic approaches the true minimum at an optimal rate. This intuitive viewpoint reveals clear connections to the original fast-gradient methods and cutting plane ideas, and leads to limited-memory extensions with improved performance.
Joint work with Dmitriy Drusvyatskiy and Maryam Fazel.