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..
Algorithms for Optimizing the Ratio of Submodular Functions
Authors: Wenruo Bai, Rishabh Iyer, Kai Wei, Jeff Bilmes
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically demonstrate the performance and good scalability properties of our algorithms. ... 6. Experiments: We empirically evaluate our proposed algorithmic frameworks for RS minimization, including in particular MMIN, GREEDRATIO, and ELLIPSOIDAPPROX, on a synthetic data set. |
| Researcher Affiliation | Academia | Wenruo Bai EMAIL Rishabh Iyer EMAIL Kai Wei EMAIL Jeff Bilmes EMAIL University of Washington, Seattle, WA 98195, USA |
| Pseudocode | Yes | Algorithm 1 A (1 + ϵ)-approximation algorithm for RS minimization using an exact algorithm for DS minimization. ... Algorithm 2 Approx. algorithm for RS minimization using an approximation algorithm for SCSC. ... Algorithm 3 GREEDRATIO for RS minimization. ... Algorithm 4 ELLIPSOIDAPPROX. ... Algorithm 5 MMIN for RS minimization. |
| Open Source Code | No | The paper does not provide any statement or link indicating that open-source code for the described methodology is available. |
| Open Datasets | No | We empirically evaluate our proposed algorithmic frameworks for RS minimization, including in particular MMIN, GREEDRATIO, and ELLIPSOIDAPPROX, on a synthetic data set. In particular, we evaluate on a generalized form of the F-measure function: Fλ(X) = |Γ(X) T| λ|T| + (1 λ)|Γ(X)|, (32) where 0 λ 1 is a parameter that determines a tradeoff weight between precision and recall. Note Fλ=0.5 is the same as the F-measure function defined in Eqn. 3. We instantiate the F-measure function on a randomly generated bipartite graph G(U, W, E). The bipartite graph is defined with |U| = 100 and |W| = 100. We define an edge between u U and w W independently with probability p = 0.05. The set of targets T W is also randomly chosen with a fixed size 20, i.e., |T| = 20. We run the experiments on 10 instances of the randomly and independently generated data, and we report the averaged results. |
| Dataset Splits | No | The paper describes the generation of synthetic data for evaluation but does not specify distinct training, validation, or test splits for a model or algorithm, as the algorithms themselves are being evaluated rather than a trained model. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers. |
| Experiment Setup | Yes | We instantiate the F-measure function on a randomly generated bipartite graph G(U, W, E). The bipartite graph is defined with |U| = 100 and |W| = 100. We define an edge between u U and w W independently with probability p = 0.05. The set of targets T W is also randomly chosen with a fixed size 20, i.e., |T| = 20. We run the experiments on 10 instances of the randomly and independently generated data, and we report the averaged results. ... As a baseline, we also implement a random sampling method, where we randomly choose 100 subsets X U with size |X| = 50 and report their averaged function valuation in terms of Fλ and their standard deviation. In Figure 1, we compare the performance of all methods on with the varying λ. |