Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Authors: Francesco Locatello, Michael Tschannen, Gunnar Raetsch, Martin Jaggi
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the performance of the presented algorithms on three different exemplary tasks, showing that our algorithms are competitive with established baselines across a wide range of objective functions, domains, and data sets while not being specifically tailored to any of these tasks (see Section 3.2 for a discussion of the computational complexity of the algorithms). Additional experiments targeting KL divergence NMF, non-negative tensor factorization, and hyperspectral image unmixing can be found in the appendix.6 Illustrative Experiments |
| Researcher Affiliation | Academia | Francesco Locatello MPI for Intelligent Systems ETH Zurich locatelf@ethz.ch Michael Tschannen ETH Zurich michaelt@nari.ee.ethz.ch Gunnar Rätsch ETH Zurich raetsch@inf.ethz.ch Martin Jaggi EPFL martin.jaggi@epfl.ch |
| Pseudocode | Yes | Algorithm 1 Norm-Corrective Generalized Matching Pursuit, Algorithm 2 Non-Negative Matching Pursuit, Algorithm 3 Away-steps (AMP) and Pairwise (PWMP) Non-Negative Matching Pursuit, Algorithm 4 Fully Corrective Non-Negative Matching Pursuit (FCMP) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We consider three different datasets: The Reuters Corpus2, the CBCL face dataset3 and the KNIX dataset4. 2http://www.nltk.org/book/ch02.html 3http://cbcl.mit.edu/software-datasets/Face Data2.html 4http://www.osirix-viewer.com/resources/dicom-image-library/ |
| Dataset Splits | No | The paper mentions 'training and test accuracy on 100 random splits' for the non-negative garrote experiment but does not provide specific percentages, sample counts, or explicit validation splits for dataset partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the general setup of the experiments and the algorithms being compared but does not specify concrete hyperparameter values or detailed training configurations for its models. |