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Ph.D. Defense: Guannan Liang

April 1, 2021 @ 10:00 am - 11:30 am EDT

Doctoral Dissertation Oral Defense

Title: Efficient and Privacy-preserving Algorithms for Nonconvex Sparse Learning

Ph.D. Candidate: Guannan Liang

Major Advisor:  Dr. Jinbo Bi

Associate Advisors:  Dr. Sanguthevar Rajasekaran, Dr. Alexander Russell

Review Committee Members: Dr. Yufeng Wu and Dr. Laurent Michel

Date/Time: Thursday, April 1, 2021, 10:00 am


Meeting link:  https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m4b4a29775e1591afd9240f51aca824a1

Join by phone: +1-415-655-0002 

Access code: 242 915 21




Sparse learning plays important roles in the fields of statistical learning, machine learning (ML) and signal processing. Learning with high dimensional data often relies on the sparsity-driven regularization. Solving a sparsity-regularized empirical risk minimization (ERM) problem  can derive a ML model with sparse parameters,  meaning that the model parameter vector has many zero entries. Thus, it helps select relevant features for use in a ML model. For example,  via the sparsity regularization, genomic analysis can identify a small number of genes contributing to the risk of a disease, and smartphone-based healthcare systems can detect the most important mobile health indicators. In this dissertation, sparse learning is formulated as nonconvex and/or nonsmooth optimization problems depending on the specific regularizer. We develop efficient stochastic gradient descent (SGD) based algorithms to solve these problems. In order for sparse learning to help privacy-preserving data mining, we develop another set of algorithms that satisfy a mathematical condition namely differential privacy (DP).


First, we propose a hard thresholding (HT) method based on the stochastically controlled stochastic gradient descent (SCSG) algorithm, which we call SCSG-HT, to solve constrained ERM problems with a constraint on the L0-norm of the model parameter vector. SCSG belongs to a family of stochastic variance reduction gradient (SVRG) methods that attempt to reduce the variance of the stochastic gradients used in the optimization process. We prove that SCSG-HT has a stronger guarantee than SGD-based HT methods to recover the optimal sparse estimator. The computational complexity of SCSG-HT is independent of sample size n when n is large, which enhances the scalability to massive-scale problems.


Second, to solve the unconstrained ERM problem where the sparsity constraint becomes a non-smooth penalty term in the objective, we design a family of stochastic proximal gradient methods.   Prior proximal methods uniformly draw mini-batches to calculate stochastic gradients. Our methods draw samples according to an arbitrary probability distribution. A family of methods can be developed according to different sample schemes in SGD and its variance-reduction variants.  A unified  approach is developed to analyze the convergence and computational complexity of these methods across different sampling schemes, and allows us to compare the schemes. We show that the independent sampling method tends to improve performance over the commonly-used uniform sampling method. Our new analysis also arrives at a tighter bound on convergence speed for the uniform sampling than the best one known so far.


Third, we solve sparse learning problems with privacy preserving guarantee. Specifically, we develop algorithms that satisfy DP, including DP-SGD-HT and DP-SCSG-HT. A DP algorithm  based on the non-stochastic gradient descent method has previously developed for the sparsity-regularized ERM problem, but it is computationally prohibitive due to the cost of calculating full gradients. Our DP-SGD-HT method uses stochastic gradients and is thus much more efficient and scalable. DP-SCSG-HT further reduces the estimation error by reducing the stochasticity caused by the mini-batch sampling in SGD steps, under the same DP guarantee. For both DP-SGD-HT and DP-SCSG-HT, we provide their theoretical analyses in terms of convergence, utility bound, and computational complexity.


April 1, 2021
10:00 am - 11:30 am EDT

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