Tracking Most Significant Shifts in Infinite-Armed Bandits
Authors: Joe Suk, Jung-Hun Kim
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our findings via experiments on synthetic data, showing our procedures perform better than the previous art for rotting infinite-armed bandits. Here, we demonstrate the performance of Algorithms 1 and 2 on synthetic datasets. |
| Researcher Affiliation | Academia | 1Columbia University 2CREST, ENSAE Paris. Correspondence to: Joe Suk <EMAIL>. |
| Pseudocode | Yes | Algorithm 1: Blackbox Non-Stationary Algorithm Algorithm 2: Restarting Subsampling Elimination |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the methodology described in this paper. |
| Open Datasets | No | Here, we demonstrate the performance of Algorithms 1 and 2 on synthetic datasets. For the Base-Alg in Algorithm 1 we use UCB (Auer et al., 2002). ... Comparing Total Variation-based Regret Bounds. To ensure a fair comparison between algorithms, we consider a rotting scenario where the mean reward of each selected arm decreases at a rate of O(1/t) at time t. In this environment, for all our algorithms, it can be shown that L = Ω(T), V = O(1). |
| Dataset Splits | No | The paper mentions using "synthetic datasets" but does not provide any information about how these datasets were generated or split (e.g., training, validation, test splits) for the experiments. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | For the Base-Alg in Algorithm 1 we use UCB (Auer et al., 2002). ... As benchmarks, we implement SSUCB (Bayati et al., 2020) and a variant of it using a sliding window with size T (SSUCB-SW), and AUCBT-ASW (Kim et al., 2024). The paper names specific algorithms but does not provide version numbers for any software, libraries, or environments used in the implementation. |
| Experiment Setup | No | The paper describes the scenarios for the synthetic datasets (e.g., "rotting scenario where the mean reward of each selected arm decreases at a rate of O(1/t)", "piecewise stationary setting"). However, it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rates, batch sizes, specific UCB parameters), model initialization, or training configurations. |