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.
PRODuctive bandits: Importance Weighting No More
Authors: Julian Zimmert, Teodor Vanislavov Marinov
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 28,585 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,078 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | By leveraging the interpretation of Prod as a first-order OMD approximation, we present the following surprising results: 1. Variants of Prod can obtain optimal regret for adversarial multi-armed bandits. 2. There exists a simple and (arguably) importance-weighting free variant with optimal rate. 3. One can even achieve best-both-worlds guarantees with logarithmic regret in the stochastic regime. |
| Researcher Affiliation | Industry | Julian Zimmert Google Research EMAIL Teodor V. Marinov Google Research EMAIL |
| Pseudocode | No | The paper describes algorithm update rules within the text (e.g., 'WSU-UX uses importance-weighted updates... πt+1,i = πt,i(1 η(ˆℓt,i λt))'), but it does not contain clearly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: No experiments requiring code. |
| Open Datasets | No | The paper is purely theoretical and does not conduct experiments with datasets. |
| Dataset Splits | No | The paper is purely theoretical and does not conduct experiments, so it does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [NA] Justification: No experiments. |
| Software Dependencies | No | The paper is purely theoretical and does not involve experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not involve experiments, thus no experimental setup details such as hyperparameters or system-level training settings are provided. |