Robust Lipschitz Bandits to Adversarial Corruptions
Authors: Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we show by simulations that our proposed RMEL and Bo B Robust Zooming algorithm outperform the classic Zooming algorithm in the presence of adversarial corruptions. Average cumulative regrets over 20 repetitions are reported in Figure 1. |
| Researcher Affiliation | Collaboration | Yue Kang Department of Statistics University of California, Davis Davis, CA 95616 yuekang@ucdavis.edu Cho-Jui Hsieh Google and Department of Computer Science, UCLA Los Angeles, CA chohsieh@cs.ucla.edu Thomas C. M. Lee Department of Statistics University of California, Davis Davis, CA 95616 tcmlee@ucdavis.edu |
| Pseudocode | Yes | Algorithm 1 Robust Zooming Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for their methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper conducts simulations using synthetic mean functions (triangle, sine, two dim) and does not refer to publicly available datasets or provide access information for any data used. |
| Dataset Splits | No | The paper conducts simulations for a bandit problem, which involves sequential interaction rather than fixed dataset splits for training, validation, and testing. Therefore, it does not provide specific dataset split information. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions “Pyxab a python library” as a reference, but it does not provide specific software dependency details with version numbers (e.g., Python 3.x, PyTorch 1.x) for its own implementation. |
| Experiment Setup | Yes | We set the time horizon T = 50, 000 (60, 000) for the metric space with d = 1 (2) and the false probability rate δ = 0.01. The random noise at each round is sampled IID from N(0, 0.01). Specifically, we set C = 0 for the non-corrupted case, C = 3, 000 for the moderate-corrupted case and C = 4, 500 for the strong-corrupted case. |