Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
Authors: Qixin Zhang, Zengde Deng, Zaiyi Chen, Haoyuan Hu, Yu Yang
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, numerical experiments demonstrate the effectiveness of our boosting methods. |
| Researcher Affiliation | Collaboration | 1School of Data Science City University of Hong Kong, Kowloon Hong Kong, China 2 Cainiao Network, Hang Zhou, China. |
| Pseudocode | Yes | Algorithm 1 Meta Boosting Protocol ... Algorithm 2 Boosting Gradient Ascent ... Algorithm 3 Online Boosting Delayed Gradient Ascent |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | Hassani et al. (2017) introduced a special continuous DRsubmodular function fk coming from the multilinear extension of a set cover function. ... Following (Bian et al., 2017), we choose the matrix H ∈ R^n×n to be a randomly generated symmetric matrix with entries uniformly distributed in [−1, 0], and the matrix A to be a random matrix with entries uniformly distributed in [0, 1]. |
| Dataset Splits | No | The paper does not explicitly mention the use of a validation set or specific data splits for training, validation, and testing. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | In our experiment, we set k = 15 and consider a standard Gaussian noise, i.e., e~f(x) = f(x) + N(0, 1). ... We set b = u = 1, m = 12, and n = 25. To ensure the monotonicity, we set h = H^T u. ... Similarly, we also consider the Gaussian noise for gradient, i.e., e~f(x) = f(x) + δN(0, 1). We consider δ = 5 and start all algorithms from the origin. ... We also add the Gaussian noise for the gradient of each ft, i.e., e~ft(x) = ft(x) + δN(0, 1) with δ = 5. To simulate the feedback delays, we generate a uniform random number dt from {1, 2, 3, 4, 5} for the stochastic gradient information of ft. ... with step size ηt = 1/√t. ... with ρt = 1/(t+3)^2/3 ... where we set the minibatch size |M0| = T^2 and |M| = T for T-round iterations. ... with step size 1/√T. ... with the step size ηt = 1/√T and use the average of B independent samples to estimate the gradient at each round. |