Active Learning for Distributionally Robust Level-Set Estimation
Authors: Yu Inatsu, Shogo Iwazaki, Ichiro Takeuchi
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that the proposed method has theoretical guarantees on convergence and accuracy, and confirmed through numerical experiments that the proposed method outperforms existing methods. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Nagoya Institute of Technology, Aichi, Japan 2RIKEN Center for Advanced Intelligence Project, Tokyo, Japan. |
| Pseudocode | Yes | Algorithm 1 Active learning for distributionally robust level-set estimation |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper defines mathematical functions (Booth, Matyas, McCormick, Styblinski-Tang) for synthetic data experiments and uses the SIR model for infection simulations, rather than using publicly available datasets. No concrete access information for any dataset is provided. |
| Dataset Splits | No | The paper does not specify traditional training, validation, or test dataset splits. It describes an active learning setup where data points are sequentially selected and added to a growing training set for a Gaussian Process model. Evaluation is based on F-score for the identified level set, not a predefined split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions using a 'Gaussian process (GP) model' and a 'Gaussian kernel' but does not specify any particular software libraries or their version numbers (e.g., PyTorch, TensorFlow, scikit-learn, GPy) used for implementation. |
| Experiment Setup | Yes | For the infection simulations, the paper specifies parameters: 'h = 135, α = 0.9, σ2 = 0.025, σ2 f = 2502, L = 0.5, β1/2 t = 4, ϵ = 0.05'. It also mentions that 'parameters used for each experiment are listed in Table 2 in the Appendix' (which is not provided in the given text, but the explicit reference counts). For general setup, it states: 'Here, for simplicity, we set the accuracy parameter η to zero. Similarly, because of the computational cost of calculating acquisition functions, we replaced P(y Rs)1l[l(F ) t (x; 0|x , w , cs) > α] in (3.3) with zero when P(y Rs) satisfies P(y Rs) < 0.005. In other words, we used Lemma 3.3 with ζ/(|Ω| + 1) = 0.005 to approximate (3.3).' |