Yangyang Xu
Home
Biography
Research
Summary
Papers
Group
Talks
Software
Teaching
Stochastic Optimization
Nonlinear Programming
Computational Optimization
Links
Math Institutes
Conferences

Stochastic Optimization and Reinforcement Learning, Fall 2022

Course Outline

Time and location

Time: Tuesday, Friday 2:00 - 3:50pm

Location: Carnegie 208

Instructor

Yangyang Xu

Office: Amos Eaton 310

Office hour: in person, 3:50pm - 4:50pm on Tuesday and Friday or by appointment

Email: xuy21@rpi.edu

Assignments

Reading materials

  • On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. Lin, Jin, Jordan. ICML, 2020.

  • Adaptive Primal-Dual Stochastic Gradient Method for Expectation-constrained Convex Stochastic Programs. Yan and Xu. Math. Programming Computation, 2022.

  • Primal-dual stochastic gradient method for convex programs with many functional constraints, Xu, SIOPT, 2020.

  • Algorithms for stochastic optimization with function or expectation constraints, Lan and Zhou, COAP, 2020.

  • Escaping Saddles with Stochastic Gradients, Hadi Daneshmand, Jonas Kohler, Aurelien Lucchi, Thomas Hofmann, ICML, 2018.

  • First-order methods almost always avoid strict saddle points, Jason D. Lee, Ioannis Panageas, Georgios Piliouras, Max Simchowitz, Michael I. Jordan, Benjamin Recht. Math. Programming, 2019.

  • Distributed Learning Systems with First-Order Methods, Liu and Zhang. Foundations and Trends in Databases, 2020.

  • A hybrid stochastic optimization framework for composite nonconvex optimization. Tran-Dinh, Pham, Phan, and Nguyen. Mathematical Programming, 2022.

  • Spiderboost and momentum: Faster variance reduction algorithms. Wang, Ji, Zhou, Liang, and Tarokh. NeurIPS, 2019.

  • Momentum-based variance-reduced proximal stochastic gradient method for composite nonconvex stochastic optimization. Xu and Xu, 2022.

  • Stochastic model-based minimization of weakly convex functions, David and Drusvyatskiy, SIAM J. On Optimization, 2019.

  • iPiano: Inertial proximal algorithm for nonconvex optimization, Ochs, Chen, Brox, and Pock, SIAM J. On Imaging Sciences, 2014.

  • Gradient methods for minimizing composite functions, Nesterov, Math Programming, 2013.