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..
Robust Budget Allocation via Continuous Submodular Functions
Authors: Matthew Staib, Stefanie Jegelka
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our Robust Budget Allocation algorithm on both synthetic test data and a real-world bidding dataset from Yahoo! Webscope (yah) to demonstrate that our method yields real improvements. For all experiments, we used Algorithm 1 as the outer loop. For the inner submodular minimization step, we implemented the pairwise Frank-Wolfe algorithm of (Lacoste-Julien & Jaggi, 2015). |
| Researcher Affiliation | Academia | Matthew Staib 1 Stefanie Jegelka 1 1Massachusetts Institute of Technology. |
| Pseudocode | Yes | Algorithm 1 Subgradient Ascent |
| Open Source Code | Yes | Our code is available at git.io/v HXk O. |
| Open Datasets | Yes | To evaluate our method on real-world data, we formulate a Budget Allocation instance on advertiser bidding data from Yahoo! Webscope (yah). This dataset logs bids on 1000 different phrases by advertising accounts. ... Yahoo! Webscope dataset ydata-ysm-advertiser-bids-v1 0. URL http://research.yahoo.com/ Academic_Relations. |
| Dataset Splits | No | The paper evaluates on 'synthetic test data' and 'real-world bidding dataset from Yahoo! Webscope', but it does not specify train/validation/test dataset splits, percentages, or absolute sample counts for these splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. It only vaguely mentions 'MIT Supercloud and the Lincoln Laboratory Supercomputing Center'. |
| Software Dependencies | Yes | implementing the linear oracle using MOSEK (MOSEK Ap S, 2015). (In references): MOSEK Ap S. MOSEK MATLAB Toolbox 8.0.0.57, 2015. URL http://docs.mosek.com/8.0/ toolbox/index.html. |
| Experiment Setup | Yes | For all experiments, we used Algorithm 1 as the outer loop. For the inner submodular minimization step, we implemented the pairwise Frank-Wolfe algorithm of (Lacoste-Julien & Jaggi, 2015). In all cases, the feasible set of budgets Y is {y 2 RS s2S y(s) C} where the specific budget C depends on the experiment. Our code is available at git.io/v HXk O. For both settings, we set |S| = 6 and |T| = 2 and discretize with δ = 0.001. |