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..
Delay as Payoff in MAB
Authors: Ofir Schlisselberg, Ido Cohen, Tal Lancewicki, Yishay Mansour
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we accompany our theoretical results with an empirical evaluation. We conducted synthetic experiments for both the cost and reward settings, using the algorithms in Table 1 as baselines. We show results on two representative distributions: Truncated Normal (bounded in [0, D]) and Bernoulli. ... Figure 1 shows the average cumulative regret over 10 runs. |
| Researcher Affiliation | Collaboration | 1Tel Aviv University 2Google Research EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Protocol1 ... Algorithm 2 Cost Successive Elimination (CSE) ... Algorithm 3 Bounded Doubling Successive Elimination ... Algorithm 4 Reward Successive Elimination ... Algorithm 5 Bounded Halving Successive Elimination (BHSE) |
| Open Source Code | No | The paper does not contain an explicit statement about releasing code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | We conducted synthetic experiments for both the cost and reward settings... For the truncated Normal we sample K means and standard deviations (std)... For the Bernoulli distribution, we sample K probabilities pi uniformly in [0, 1]... |
| Dataset Splits | No | The paper states parameters for synthetic data generation (e.g., T=150,000, K=30, D=5000) but does not provide specific training/test/validation dataset splits, cross-validation, or other data partitioning details. |
| 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 | The paper does not provide specific version numbers for any software components, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | All experiments use T=150, 000, K=30 and D=5000. For the truncated Normal we sample K means and standard deviations (std), and adjust them to get a truncated version... For the Bernoulli distribution, we sample K probabilities pi uniformly in [0, 1]... Figure 1 shows the average cumulative regret over 10 runs. |