Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
Authors: Sanae Amani, Tor Lattimore, András György, Lin Yang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical results with numerical simulations in Section 5. 5. Experiments In this section, we present numerical simulations to confirm our theoretical findings. |
| Researcher Affiliation | Collaboration | 1Department of Electrical and Computer Engineering, University of California, Los Angeles. 2Deep Mind, London. |
| Pseudocode | Yes | Algorithm 1 Dis BE-LUCB for agent i, Algorithm 2 Dec BE-LUCB for agent i, Algorithm 3 Comm for Agent i, Algorithm 4 Exp Pol, Algorithm 5 Core Identification (Algorithm 4 in (Ruan et al., 2021)), Algorithm 6 Mixed Soft Max. |
| Open Source Code | No | No explicit statement or link providing concrete access to the source code for the methodology described in this paper was found. |
| Open Datasets | No | The decision set distribution D is chosen to be uniform over { X1, X2, . . . , X100}, where each Xi is a set of K vectors drawn from N(0, Id) and then normalized to unit norm. This is a description of synthetic data generation, not a publicly available dataset with concrete access information. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning was found. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | In this section, we present numerical simulations to confirm our theoretical findings. We evaluate the performance of Dis BE-LUCB on synthetic data and compare it to that of Dis Lin UCB proposed by Wang et al. (2019). The results shown in Figure 1 depict averages over 20 realizations, for which we have chosen K = 20, δ = 0.01 and T = 100000. For each realization, the parameter θ is drawn from N(0, Id) and then normalized to unit norm and noise variables are zero-mean Gaussian random variables with variance 0.01. The decision set distribution D is chosen to be uniform over { X1, X2, . . . , X100}, where each Xi is a set of K vectors drawn from N(0, Id) and then normalized to unit norm. |