Safeguarded Learned Convex Optimization
Authors: Howard Heaton, Xiaohan Chen, Zhangyang Wang, Wotao Yin
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical examples show convergence of Safe-L2O algorithms, even when the provided data is not from the distribution of training data. This section presents examples using Safe-L2O.2 We numerically investigate i) the convergence rate of Safe-L2O relative to corresponding conventional algorithms, ii) the efficacy of safeguarding procedures when inferences are performed on data for which L2O fails intermittently, and iii) the convergence of Safe-L2O schemes even when the application of L2O operators is not theoretically justified. |
| Researcher Affiliation | Collaboration | Howard Heaton*1, Xiaohan Chen*2, Zhangyang Wang2, Wotao Yin3 1Typal Research, Typal LLC 2Department of Electrical and Computer and Engineering, The University of Texas at Austin 3Alibaba US, DAMO Academy, Decision Intelligence Lab |
| Pseudocode | Yes | Algorithm 1 L2O Network (No Safeguard); Algorithm 2 Safeguarded L2O (Safe-L2O) |
| Open Source Code | Yes | Code is on Git Hub: github.com/VITA-Group/Safe L2O |
| Open Datasets | Yes | The dictionary A R256 512 is learned on the BSD500 dataset (Martin et al. 2001) by solving a dictionary learning problem (Xu and Yin 2014). |
| Dataset Splits | No | The appropriate frequency for the safeguard to trigger can be estimated by tuning L2O parameters for optimal performance on a training set without safeguarding and then using a validation set to test various safeguards with the L2O scheme. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper discusses various algorithms and frameworks but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Inferences used α = 0.99 and EMA(0.25). As Safe-L2O convergence holds whenever β > 0, we can set β to be arbitrarily small (e.g. below machine precision); for simplicity, we use β = 0 in the experiments. Specifically, we let x R70 be sparse vectors with random supports of cardinality s = 6 and a single fixed dictionary A R50 70. |