Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
Authors: Qixin Zhang, Zengde Deng, Zaiyi Chen, Haoyuan Hu, Yu Yang
ICML 2022 | Venue PDF | 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. |