Student-t Process Regression with Student-t Likelihood
Authors: Qingtao Tang, Li Niu, Yisen Wang, Tao Dai, Wangpeng An, Jianfei Cai, Shu-Tao Xia
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Various experiments demonstrate that TPRT outperforms GPR and its variants on both synthetic and real datasets. 5 Experiments In this section, we evaluate our TPRT method on both synthetic and real datasets. The experimental results demonstrate the effectiveness of our TPRT method. |
| Researcher Affiliation | Academia | Department of Computer Science and Technology, Tsinghua University, China School of Computer Science and Engineering, Nanyang Technological University, Singapore |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | No | No statement regarding open-source code availability or links to a code repository were found. |
| Open Datasets | Yes | For the synthetic datasets, we use Neal Dataset [Neal, 1997] and its variant with input outliers. ... For real datasets, we use 6 real datasets from [Alcal a et al., 2010; Lichman, 2013] |
| Dataset Splits | No | For each dataset, 80% of instances are randomly sampled as the training data and the remaining instances are used as the test data. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned. |
| Software Dependencies | No | The main computational cost of TPRT lies in solving the inverse of the kernel matrix (O(n3) time complexity), which can be accelerated by most methods speeding up GPR, such as methods proposed by [Williams and Seeger, 2000; Deisenroth and Ng, 2015; Wilson and Nickisch, 2015]. ... Our implementation is also based on the conjugate gradient optimization. |
| Experiment Setup | Yes | The initial values for the kernel parameters θk and the variance of the noise σ2 are set as 1. Note that we use the same initial θk and σ2 for the baselines GPR and GPRT. The main computational cost of TPRT lies in solving the inverse of the kernel matrix (O(n3) time complexity), which can be accelerated by most methods speeding up GPR, such as methods proposed by [Williams and Seeger, 2000; Deisenroth and Ng, 2015; Wilson and Nickisch, 2015]. ... Recall that when learning the hyper-parameters, we use conjugate gradient method, in which the maximum iteration number is set as 100. |