Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Robust Lipschitz Bandits to Adversarial Corruptions
Authors: Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee
NeurIPS 2023 | Venue PDF | 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 EMAIL Cho-Jui Hsieh Google and Department of Computer Science, UCLA Los Angeles, CA EMAIL Thomas C. M. Lee Department of Statistics University of California, Davis Davis, CA 95616 EMAIL |
| 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. |