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.
Differentially-Private Federated Linear Bandits
Authors: Abhimanyu Dubey, Alex `Sandy' Pentland
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 19,551 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,196 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 | 4.4 Experiments. Figure 1: A comparison of centralized FEDUCB on 3 different axes. Fig. (A) describes the variation in asymptotic per-agent regret for varying privacy budget ε (where δ = 0.1); (B) describes the effect of n in private (solid) vs. non-private (dashed) settings; (C) describes the effect of d in per-agent regret in the private setting (n = O(M log T), ε = 1, δ = 0.1). Experiments averaged over 100 runs. |
| Researcher Affiliation | Academia | Abhimanyu Dubey and Alex Pentland Media Lab and Institute for Data, Systems and Society Massachusetts Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1 CENTRALIZED FEDUCB(D, M, T, ρmin, ρmax). Algorithm 2 PRIVATIZER(ε, δ, M, T) for any agent i |
| Open Source Code | No | No statement regarding open-source code availability or links to repositories was found. |
| Open Datasets | No | For any d, we randomly fix θ Bd(1). Each Di,t is generated as follows: we randomly sample K d2 actions x, such that for K 1 actions 0.5 x, θ 0.6 and for the optimal x , 0.7 x , θ 0.8 such that 0.1 always. yi,t is sampled from Ber( xi,t, θ ) such that E[yi,t] = xi,t, θ and |yi,t| 1. |
| Dataset Splits | No | The paper describes generating synthetic data for a sequential learning problem but does not specify traditional train/validation/test dataset splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. |
| Experiment Setup | Yes | For all experiments, we assume L = S = 1. For any d, we randomly fix θ Bd(1). Each Di,t is generated as follows: we randomly sample K d2 actions x, such that for K 1 actions 0.5 x, θ 0.6 and for the optimal x , 0.7 x , θ 0.8 such that 0.1 always. yi,t is sampled from Ber( xi,t, θ ) such that E[yi,t] = xi,t, θ and |yi,t| 1. Results are in Fig. 1, and experiments are averaged on 100 trials. In this setting, we set n = O(M log T), d = 10 (to balance communication and performance), and plot the average per-agent regret after T = 10^7 trials for varying M and ε, while keeping δ = 0.1. |