Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces
Authors: Akash Saha, Balamurugan Palaniappan
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with indefinite operator-valued kernels on synthetic and real data sets demonstrate the utility of the proposed approach. |
| Researcher Affiliation | Academia | Akash Saha IEOR, IIT Bombay Mumbai, India akashsaha@iitb.ac.in P. Balamurugan IEOR, IIT Bombay Mumbai, India balamurugan.palaniappan@iitb.ac.in |
| Pseudocode | Yes | Algorithm 1: Op MINRES |
| Open Source Code | Yes | All methods were coded in Python 3.6 and the codes are made public1. 1The codes used for experiments can be found at https://github.com/akashsaha06/NeurIPS-2020/ |
| Open Datasets | Yes | We use the Haskins IEEE Rate Comparison DB dataset available at https://yale.app.box.com/s/cfn8hj2puveo65fq54rp1ml2mk7moj3h (Tiede et al., 2017). |
| Dataset Splits | Yes | The experiments performed used 320 samples for training and 80 samples for testing. For hyperparameter tuning, we used 3-fold multi-grid cross validation for all the methods. |
| Hardware Specification | No | All experiments were run on a Linux box with 182 Gigabytes main memory and 28 CPU cores. While memory and core count are given, specific CPU models or types are not mentioned, which is required for detailed hardware specification. |
| Software Dependencies | Yes | All methods were coded in Python 3.6 |
| Experiment Setup | Yes | λ was chosen from {10 3, 10 2, 0.1, 1, 10, 100}. γ, γ1, γ2, η, η1, η2 were chosen from {0.001, 0.002, . . . , 0.009, 0.01, 0.02, . . . , 0.09, 0.1, 0.2, . . . , 0.9, 1, 2, . . . , 10, 20, . . . , 100}. |