An Optimal Stochastic Compositional Optimization Method with Applications to Meta Learning
- Resource Type
- Conference
- Authors
- Sun, Yuejiao; Chen, Tianyi; Yin, Wotao
- Source
- ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2021 - 2021 IEEE International Conference on. :3665-3669 Jun, 2021
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Gradient methods
Conferences
Stochastic processes
Machine learning
Signal processing
Minimization
Task analysis
Stochastic optimization
compositional optimization
stochastic gradient descent
model-agnostic meta-learning
- Language
- ISSN
- 2379-190X
Stochastic compositional optimization generalizes classic (non-compositional) stochastic optimization to the minimization of com-positions of functions. Each composition may introduce an additional expectation. The series of expectations may be nested. Stochastic compositional optimization is gaining popularity in applications such as meta learning. This paper presents a new Stochastically Corrected Stochastic Compositional gradient method (SCSC). SCSC runs in a single-time scale with a single loop, uses a fixed batch size, and guarantees to converge at the same rate as the stochastic gradient descent (SGD) method for non-compositional stochastic optimization. It is easy to apply SGD-improvement techniques to accelerate SCSC. This helps SCSC achieve state-of-the-art performance for stochastic compositional optimization. In particular, we apply Adam to SCSC, and the exhibited rate of convergence matches that of the original Adam on non-compositional optimization. We test SCSC using the model-agnostic meta-learning tasks.