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.
UCB-based Algorithms for Multinomial Logistic Regression Bandits
Authors: Sanae Amani, Christos Thrampoulidis
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,695 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,042 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 present numerical simulations to complement and confirm our theoretical findings. We evaluate the performance of MNL-UCB on synthetic data. |
| Researcher Affiliation | Academia | Sanae Amani University of California, Los Angeles EMAIL Christos Thrampoulidis University of British Columbia EMAIL |
| Pseudocode | Yes | Algorithm 1: MNL-UCB 1 for t = 1, . . . , T do 2 Compute t as in (21). 3 Compute xt := arg maxx2D T z(x, t) + t(x) with t(x) deο¬ned in (22). 4 Play xt and observe yt. |
| Open Source Code | No | No explicit statement about providing open-source code or a link to a code repository was found. |
| Open Datasets | No | We evaluate the performance of MNL-UCB on synthetic data. |
| Dataset Splits | No | No specific dataset split information (percentages, sample counts, or predefined splits) was provided. The paper mentions using 'synthetic data' and '20 realizations' but no explicit train/validation/test splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments were provided. |
| Software Dependencies | No | No specific ancillary software details with version numbers were provided. |
| Experiment Setup | Yes | In all experiments, we used the upper bound on in (26) to compute the exploration bonus t(x). We evaluate the performance of MNL-UCB on synthetic data. All the results shown depict averages over 20 realizations, for which we have chosen Ξ΄ = 0.01, d = 2, and T = 1000. We considered time-independent decision sets D of 20 arms in R2 and the reward vector = [1, . . . , K]T . Moreover, the arms and i are drawn from N(0, Id) and N(0, Id/K), respectively. |