This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization
Authors: Anthony Bardou, Patrick Thiran, Giovanni Ranieri
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments establish the superiority of W-DBO, which outperforms state-of-the-art methods by a comfortable margin. |
| Researcher Affiliation | Academia | Anthony Bardou IC, EPFL Lausanne, Switzerland anthony.bardou@epfl.ch Patrick Thiran IC, EPFL Lausanne, Switzerland patrick.thiran@epfl.ch Giovanni Ranieri IC, EPFL Lausanne, Switzerland giovanni.ranieri@epfl.ch |
| Pseudocode | Yes | Algorithm 1 describes W-DBO, a DBO algorithm exploiting (12) to remove irrelevant observations on the fly. |
| Open Source Code | Yes | Its Python documented implementation can be found at https://github.com/WDBO-ALGORITHM/wdbo_ algo. A Py PI package can be quickly installed with the command pip install wdbo-algo. |
| Open Datasets | Yes | This benchmark comes from the temperature dataset collected from 46 sensors deployed at Intel Research Berkeley. It is a famous benchmark, used in other works such as [21, 22]. |
| Dataset Splits | No | The paper describes initial observations and experimental setups but does not explicitly provide details about dataset splits for training, validation, or testing. |
| Hardware Specification | Yes | All experiments have been independently replicated 10 times on a laptop equipped with an Intel Core i9-9980HK @ 2.40 GHz with 8 cores (16 threads). |
| Software Dependencies | No | The paper mentions 'BOTorch [32] (MIT License)' and 'Py Bind11 [33] (BSD License)' but does not specify their version numbers, which are required for a reproducible description of software dependencies. |
| Experiment Setup | Yes | Each DBO algorithm begins its optimization task with 15 initial observations, uniformly sampled in S 0, 1/40 . At each iteration (at time t), (i) the noise level as well as the kernel parameters are estimated, and (ii) the GP-UCB acquisition function is optimized to get the next query. Unless stated otherwise, each DBO algorithm exploits a Matern-5/2 kernel as its spatial covariance function. |