Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Leveraging Observations in Bandits: Between Risks and Benefits
Authors: Andrei Lupu, Audrey Durand, Doina Precup6112-6119
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 14,740 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,066 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide empirical results showing both great benefits as well as certain limitations inherent to observational learning in the multi-armed bandit setting. Experiments are conducted using targets satisfying theoretical assumptions with high probability, thus narrowing the gap between theory and application. and 6 Experiments |
| Researcher Affiliation | Academia | Andrei Lupu School of Computer Science Mc Gill University EMAIL Audrey Durand School of Computer Science Mc Gill University EMAIL Doina Precup School of Computer Science Mc Gill University EMAIL |
| Pseudocode | Yes | Algorithm 1 Target-UCB for rewards in [0, 1]. |
| Open Source Code | Yes | We also provide an implementation of the Target-UCB algorithm: https://github.com/ lupuandr/Target-UCB. |
| Open Datasets | Yes | The resulting dataset can be found at: https://github.com/lupuandr/Target-UCB/tree/ master/Human%20bandit%20dataset. |
| Dataset Splits | No | The paper discusses different experimental settings (e.g., 2-actions problem, different α values, multi-agent settings) and uses Bernoulli reward distributions, but does not provide specific train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU or CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not mention specific software dependencies or their version numbers (e.g., Python, PyTorch, TensorFlow versions) used in the experiments. |
| Experiment Setup | Yes | The following experiments evaluate the potential of Target UCB (C=2) in various settings. Bernoulli reward distributions are used in all experiments. Unless indicated otherwise, all results are obtained by averaging over 2000 independent runs. In all figures, shaded areas indicate one standard deviation above the mean. and The clique size is selected using Equation (6) for δ = 0.001. |