Robust Budget Allocation via Continuous Submodular Functions
Authors: Matthew Staib, Stefanie Jegelka
ICML 2017 | Conference PDF | Archive PDF | Plain Text | 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. |