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..
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
Authors: Wenruo Bai, Jeff Bilmes
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Computational Experiments We empirically test our guarantees for BP maximization subject to a cardinality constraint on contrived functions using GREEDMAX and Semi Grad. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, University of Washington, Seattle, USA 2Department of Computer Science and Engineering, University of Washington, Seattle, USA. |
| Pseudocode | Yes | Algorithm 1: GREEDMAX for BP maximization |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating the release of source code for the described methodology. |
| Open Datasets | No | For the first experiment, we let |V | = 20 set the cardinality constraint to k = 10, and partition the ground set into |V1| = |V2| = k, V1 V2 = V where V1 = {v1, v2, . . . , vk}. Let wi = 1 α h 1 α k i+1i for i = 1, 2, . . . , k. Then we define the submodular and supermodular functions as follows, f(X) = h k α|X V2| {i:vi X} wi + |X V2| g(X) = |X| β min(1 + |X V1|, |X|, k) + ϵ max(|X|, |X| + β 1 β (|X V2| k + 1)) and h(X) = λf(X)+(1 λ)g(X) for 0 α, β, λ 1 and ϵ = 1 10 5. |
| Dataset Splits | No | No explicit mention of specific dataset split percentages, sample counts, or references to predefined splits for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for computational experiments. |
| Software Dependencies | No | The paper does not provide specific details about ancillary software, including names and version numbers. |
| Experiment Setup | Yes | For the first experiment, we let |V | = 20 set the cardinality constraint to k = 10, and partition the ground set into |V1| = |V2| = k, V1 V2 = V where V1 = {v1, v2, . . . , vk}. Let wi = 1 α h 1 α k i+1i for i = 1, 2, . . . , k. Then we define the submodular and supermodular functions as follows, f(X) = h k α|X V2| {i:vi X} wi + |X V2| g(X) = |X| β min(1 + |X V1|, |X|, k) + ϵ max(|X|, |X| + β 1 β (|X V2| k + 1)) and h(X) = λf(X)+(1 λ)g(X) for 0 α, β, λ 1 and ϵ = 1 10 5. Immediately, we notice that κf = α and κg = β. In particular, we choose α, β, λ = 0, 0.01, 0.02, . . . , 1 and for all cases, we normalize h(X) using either exhaustive search so that OPT = h(X ) = 1. |