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..
Bayesian Design Principles for Frequentist Sequential Learning
Authors: Yunbei Xu, Assaf Zeevi
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement Algorithm 4 (with the legend APS in the figures) in the stochastic, adversarial and non-stationary environments. We plot expected regret (average of 100 runs) for different choices of η, and set γ = 0.001 in all experiments. |
| Researcher Affiliation | Academia | Graduate School of Business, Columbia University, New York, New York, USA. Correspondence to: Yunbei Xu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Maximizing AIR to create algorithmic beliefs |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the methodology described, nor does it include a link to a code repository. |
| Open Datasets | No | We implement Algorithm 4 (with the legend APS in the figures) in the stochastic, adversarial and non-stationary environments. We plot expected regret (average of 100 runs) for different choices of η, and set γ = 0.001 in all experiments. |
| Dataset Splits | No | The paper describes running numerical experiments for multi-armed bandits and plots 'expected regret (average of 100 runs)', but it does not specify explicit train/validation/test dataset splits. In this context, data is generated interactively rather than being pre-partitioned. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory) used to conduct the numerical experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies, libraries, or solvers with their respective version numbers that were used to implement or run the experiments. |
| Experiment Setup | Yes | We plot expected regret (average of 100 runs) for different choices of η, and set γ = 0.001 in all experiments. We find APS 1) outperforms UCB and matches TS in the stochastic environment; 2) outperforms EXP3 in the adversarial environment; and 3) outperforms EXP3 and is comparable to the clairvoyant benchmarks (that have prior knowledge of the changes) in the non-stationary environment. For this reason we say Algorithm 4 (APS) achieves the best-of-all-worlds performance. We note that the optimized choice of η in APS differ instance by instance, but by an initial tuning we typically see good results, whether we tune η optimally or not optimally. |