This is the code repository for Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics (to appear in ICML 2024).
@article{zheng2024constrained,
title={Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics},
author={Zheng, Haoyang and Du, Hengrong and Feng, Qi and Deng, Wei and Lin, Guang},
journal={International Conference on Machine Learning},
year={2024}
}
Replica exchange stochastic gradient Langevin dynamics (reSGLD) is an effective sampler for non-convex learning in large-scale datasets. However, the simulation may encounter stagnation issues when the high-temperature chain delves too deeply into the distribution tails. To tackle this issue, we propose reflected reSGLD (r2SGLD): an algorithm tailored for constrained non-convex exploration by utilizing reflection steps within a bounded domain. Theoretically, we observe that reducing the diameter of the domain enhances mixing rates, exhibiting a quadratic behavior. Empirically, we test its performance through extensive experiments, including identifying dynamical systems with physical constraints, simulations of constrained multi-modal distributions, and image classification tasks. The theoretical and empirical findings highlight the crucial role of constrained exploration in improving the simulation efficiency.
For dynamic system identification and multi-modal simulation, please refer to "env_dynamic_multimodal.yml";
For image classification, please refer to "env_image_classification.yml".
See
./dynamic_system
See
./multimodal_simulation
See
./image_classification
Haoyang Zheng, Ph.D. candidate at the School of Mechanical Engineering, Purdue University
Email: zheng+528 at purdue dot edu