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 Greedy ++: When Upper and Lower Bounds Match
Authors: Amin Karbasi, Hamed Hassani, Aryan Mokhtari, Zebang Shen
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we develop Stochastic Continuous Greedy++ (SCG++), the first efficient variant of a conditional gradient method for maximizing a continuous submodular function subject to a convex constraint. Concretely, for a monotone and continuous DR-submodular function, SCG++ achieves a tight [(1 1/e)OPT ϵ] solution while using O(1/ϵ2) stochastic gradients and O(1/ϵ) calls to the linear optimization oracle. We further provide an information-theoretic lower bound to showcase the necessity of O(1/ϵ2) stochastic oracle queries in order to achieve [(1 1/e)OPT ϵ] for monotone and DR-submodular functions. In this section, we analyze the convergence of Algorithm 1 using (18) as the gradient-difference estimation. In this section, we show that reaching a (1 1/e ϵ)-optimal solution of Problem (1) requires at least O(1/ϵ2) calls to an oracle which provides stochastic first-order information. |
| Researcher Affiliation | Academia | Hamed Hassani ESE Department University of Pennsylvania Philadelphia, PA EMAIL; Amin Karbasi ECE Department Yale University New Haven, CT EMAIL; Aryan Mokhtari ECE Department The University of Texas at Austin Austin, TX EMAIL; Zebang Shen ESE Department University of Pennsylvania Philadelphia, PA EMAIL |
| Pseudocode | Yes | Algorithm 1 Stochastic Continuous Greedy++ (SCG++) |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments performed on any dataset, thus no information about publicly available training datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments performed on any dataset, thus no information about dataset splits for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and proofs; it does not describe any empirical experiments that would require specific hardware, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments or their implementation details, thus no software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, therefore no experimental setup details such as hyperparameters or training settings are provided. |