Armin Askari

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2022-259

December 2, 2022

Behind all supervised learning problems is an optimization problem. Solving these problems reliably and efficiently is a key step in any machine learning pipeline. This thesis looks at efficient optimization algorithms for a variety of machine learning problems (in particular, sparse learning problems). We first begin by looking at a new class of algorithms for training feedforward neural networks. We then look at an efficient algorithm for constructing knockoff features for statistical inference. Finally, we look at l0-penalized and constrained optimization problems and a class of efficient algorithms for training these non-convex problems while providing guarantees on the quality of the solution.

Advisors: Laurent El Ghaoui

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BibTeX citation:

@phdthesis{Askari:EECS-2022-259,
    Author= {Askari, Armin},
    Title= {Efficient Optimization Algorithms for Machine Learning},
    School= {EECS Department, University of California, Berkeley},
    Year= {2022},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-259.html},
    Number= {UCB/EECS-2022-259},
    Abstract= {Behind all supervised learning problems is an optimization problem. Solving these problems reliably and efficiently is a key step in any machine learning pipeline. This thesis looks at efficient optimization algorithms for a variety of machine learning problems (in particular, sparse learning problems). We first begin by looking at a new class of algorithms for training feedforward neural networks. We then look at an efficient algorithm for constructing knockoff features for statistical inference. Finally, we look at l0-penalized and constrained optimization problems and a class of efficient algorithms for training these non-convex problems while
providing guarantees on the quality of the solution.},
}

EndNote citation:

%0 Thesis
%A Askari, Armin 
%T Efficient Optimization Algorithms for Machine Learning
%I EECS Department, University of California, Berkeley
%D 2022
%8 December 2
%@ UCB/EECS-2022-259
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-259.html
%F Askari:EECS-2022-259