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..
Smooth Non-stationary Bandits
Authors: Su Jia, Qian Xie, Nathan Kallus, Peter I. Frazier
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented our algorithm with simulations on synthetic data in the one-armed setting. We visualize the regret of the two policies via a log-log plot with time horizon T = 2j where j = 20, 21, . . . , 26; see Figure 2. Theoretically, the slope of a log-log curve should equal the exponent of the cumulative regret. In fact, if the cumulative regret is c T d, then the log-regret is log c + d log T. Our simulation shows that under smooth non-stationarity, the T 3/5-regret policy outperforms the T 2/3-regret policy. Moreover, the log-log curves have slope 0.70 and 0.62 respectively, which are close to their theoretical values. |
| Researcher Affiliation | Academia | 1Cornell University, Ithaca, New York, USA. |
| Pseudocode | Yes | Algorithm 1 Budgeted Exploration Policy BE(B, ) (Section 4.1) and Algorithm 2 BE(B, ) Policy, Two-Armed Case (Section 5) |
| Open Source Code | No | The paper does not provide any link or explicit statement about making its source code publicly available. |
| Open Datasets | No | The paper uses "synthetic data" generated by the authors for simulations, not a publicly available dataset. "We implemented our algorithm with simulations on synthetic data in the one-armed setting." (Section 6) |
| Dataset Splits | No | The paper uses synthetic data for simulations and theoretical analysis of regret bounds, but does not describe explicit train/validation/test splits for a fixed dataset. |
| Hardware Specification | No | No specific hardware (e.g., GPU, CPU models, memory details) used for running the experiments is mentioned in the paper. |
| Software Dependencies | No | No specific software or library names with version numbers are mentioned (e.g., Python, PyTorch, TensorFlow, etc.) that would be necessary to replicate the experiment. |
| Experiment Setup | Yes | We implemented our algorithm with simulations on synthetic data in the one-armed setting. We consider our BE policy where the parameters are chosen to be optimal for the non-smooth and smooth environments respectively. Formally, we consider the policy BE(B, ) where the tuple (B, ) is chosen to be (T 1/3, T 1/3) for non-smooth and (T 2/5, T 1/5) for smooth non-stationary environments. ... Specifically, in each instance, we have r0(t) = A and r1(t) = A sin(2πνt/T +ϕ)+A, where ν U[2.5,5], A N(0.25ν 2, 0.001) and ϕ U[0,2π]. |