Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels
Authors: Michela Meister, Tamas Sarlos, David Woodruff
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Tensorized Random Projections in three different applications. In Section 4.1 we show that Tensorized Random Projections always succeed with high probability while Tensor Sketch always fails on extremely sparse inputs. Then in Section 4.2 we observe that Tensor Sketch and Tensorized Random Projections approximate non-linear SVMs with polynomial kernels equally well. Finally in Section 4.3 we demonstrate that Random Projections and Tensorized Random Projections are equally effective in reducing the number of parameters in a neural network while Tensorized Random Projections are faster to compute. |
| Researcher Affiliation | Collaboration | Michela Meister Cornell University Ithaca, NY 14850 meister.michela@gmail.com Tamas Sarlos Google Research Mountain View, CA 94043 stamas@google.com David P. Woodruff Department of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 dwoodruf@cs.cmu.edu |
| Pseudocode | No | The paper describes methods and analyses in prose but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for the experiments is available at https://github.com/google-research/google-research/ tree/master/poly_kernel_sketch. |
| Open Datasets | Yes | We approximate the polynomial kernel x, y 2 for the Adult [19] and MNIST [32] datasets, by applying one of the above three sketches to the dataset. |
| Dataset Splits | No | The paper mentions training models and datasets (e.g., 'train a linear SVM', 'Adult and MNIST datasets') but does not provide specific train/validation/test split percentages, sample counts, or a detailed splitting methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU/CPU models, memory, or processor types) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like LIBLINEAR, LIBSVM, and TensorFlow but does not provide specific version numbers for these or other ancillary software components. |
| Experiment Setup | No | The paper indicates that model specifics can be found in external TensorFlow tutorials and does not provide concrete hyperparameter values or detailed training configurations within the main text. |