Incremental Variational Sparse Gaussian Process Regression
Authors: Ching-An Cheng, Byron Boots
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct several experiments and show that our proposed approach achieves better empirical performance in terms of prediction error than the recent state-of-the-art incremental solutions to variational sparse GPR. |
| Researcher Affiliation | Academia | Ching-An Cheng Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta, GA 30332 cacheng@gatech.edu Byron Boots Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta, GA 30332 bboots@cc.gatech.edu |
| Pseudocode | No | The paper describes the algorithm steps mathematically and textually but does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | Yes | We performed experiments on three real-world robotic datasets datasets, kin40k4, SARCOS5, KUKA6... kin40k: 10000 training data, 30000 testing data, 8 attributes [23]. SARCOS: 44484 training data, 4449 testing data, 28 attributes. http://www.gaussianprocess.org/gpml/data/. KUKA1&KUKA2: 17560 offline data, 180360 online data, 28 attributes. [15] |
| Dataset Splits | No | The paper mentions '10000 training data, 30000 testing data' for kin40k, '44484 training data, 4449 testing data' for SARCOS, and 'split 90% into training and 10% into testing datasets' for KUKA, but no explicit mention of a separate validation set or its split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | All models were initialized with the same hyperparameters and inducing points: the hyperparameters were selected as the optimal ones in the batch variational sparse GPR [26] trained on subset of the training dataset of size 2048; the inducing points were initialized as random samples from the first minibatch. We chose the learning rate to be γt = (1 + t) 1, for stochastic mirror ascent to update the posterior approximation; the learning rate for the stochastic gradient ascent to update the hyperparameters is set to 10 4γt . We evaluate the models in terms of the normalized mean squared error (n MSE) on a held-out test set after 500 iterations. we set the number inducing functions to 512. Nm = 2048. |