Distributionally Robust Active Learning for Gaussian Process Regression
Authors: Shion Takeno, Yoshito Okura, Yu Inatsu, Aoyama Tatsuya, Tomonari Tanaka, Akahane Satoshi, Hiroyuki Hanada, Noriaki Hashimoto, Taro Murayama, Hanju Lee, Shinya Kojima, Ichiro Takeuchi
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the effectiveness of the proposed methods through synthetic and real-world datasets. 6. Experiments In this section, we demonstrate the effectiveness of the proposed methods via synthetic and real-world datasets. We employ RS, US, variance reduction (Yu et al., 2006), and expected predictive information gain (EPIG) (Bickford Smith et al., 2023) as the baseline. [...] Figure 1 shows the result. [...] Figure 2 shows the result of the expected squared error ET in the real-world data experiments with η = 0, 0.001, 0.01, 0.1. |
| Researcher Affiliation | Collaboration | 1Department of Mechanical Engineering, Nagoya University, Aichi, Japan 2Department of Computer Science, Nagoya Institute of Technology, Aichi, Japan 3RIKEN AIP, Tokyo, Japan 4DENSO CORPORATION, Aichi, Japan. |
| Pseudocode | Yes | Algorithm 1 Proposed DRAL methods Require: Domain X, GP prior µ and k, ambiguity set P 1: D0 2: for t = 1, . . . , T do 3: Update σ2 t 1( ) according to Eq. (1) 4: Compute xt according to Eq. (3) or Eq. (4) 5: end for 6: Observe y1, . . . , y T 7: Update µT ( ) and σ2 T ( ) according to Eq. (1) 8: return µT ( ) and σ2 T ( ) |
| Open Source Code | No | The paper does not contain any explicit statement about open-sourcing their code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | 6.2. Real-World Dataset Experiments We use the King County house sales2, the red wine quality (Cortez & Reis, 2009), and the auto MPG datasets (Quinlan, 1993) (See Appendix C.3 for details). For all experiments, we used SE kernels, where the hyperparameters ℓ and σ2 are adaptively determined by the marginal likelihood maximization (Rasmussen & Williams, 2005) per 10 iterations. The first input is selected uniformly at random. Furthermore, we normalize the inputs and outputs of all datasets before the experiments and set pref = N(0, 0.3Id). 2https://www.kaggle.com/datasets/ harlfoxem/housesalesprediction |
| Dataset Splits | No | The paper mentions using a "random sample of 1000 data points" for King County and states that "The first input x1 is selected uniformly at random" for both synthetic and real-world experiments. However, it does not specify explicit training, validation, and test splits (e.g., 80/10/10 split or specific counts) for evaluation of the models. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions "CVXPY (Diamond & Boyd, 2016; Agrawal et al., 2018)" as a tool used, but it does not specify a version number for CVXPY or any other software dependencies. |
| Experiment Setup | Yes | 6.1. Synthetic Data Experiemnts We set X = { 1, 0.8, . . . , 1}3, where |X| = 113 = 1331. The target function f is the sample path from GPs, where we use SE and Matérn-ν kernels with ν = 5/2. We use the fixed hyperparameters of the kernel function in the GPR model, which is used to generate f, and fix σ2 = 10 4. The first input x1 is selected uniformly at random, and T is set to 400. Furthermore, we set pref = N(0, 0.2I3). 6.2. Real-World Dataset Experiments For all experiments, we used SE kernels, where the hyperparameters ℓ and σ2 are adaptively determined by the marginal likelihood maximization (Rasmussen & Williams, 2005) per 10 iterations. The first input is selected uniformly at random. Furthermore, we normalize the inputs and outputs of all datasets before the experiments and set pref = N(0, 0.3Id). |