Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification
Authors: Junpei Komiyama, Taira Tsuchiya, Junya Honda
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section numerically tests the performance of the TNN algorithm. We compared the performance of TNN (Algorithm 4) with two algorithms: Uniform algorithm, which samples each arm in a round-robin fashion, and Successive Rejects (SR, Audibert et al., 2010), where the entire trial is divided into segments before the game starts, and one arm with the smallest estimated mean reward is removed for each segment. |
| Researcher Affiliation | Academia | Junpei Komiyama New York University New York, NY, United States junpei@komiyama.info Taira Tsuchiya Kyoto University, Kyoto, Japan RIKEN AIP, Tokyo, Japan tsuchiya@sys.i.kyoto-u.ac.jp Junya Honda Kyoto University, Kyoto, Japan RIKEN AIP, Tokyo, Japan honda@i.kyoto-u.ac.jp |
| Pseudocode | Yes | Algorithm 1: Rgo-Tracking; Algorithm 2: Delayed optimal tracking (DOT); Algorithm 3: Gradient descent method for θ; Algorithm 4: Rgo-Tracking by Neural Network (TNN) |
| Open Source Code | Yes | The source code of the simulation is available at https://github.com/tsuchhiii/fixedbudget-bai . |
| Open Datasets | No | The paper describes generating its own data instances (e.g., 'Bernoulli bandits with K = 3 arms') rather than using an established public dataset with access information or citations. |
| Dataset Splits | No | The paper describes experiments run for a fixed number of rounds (T=2000) and repeated many times, but it does not define dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running the experiments. It mentions 'See appendix' regarding compute resources, but this information is not present in the provided text. |
| Software Dependencies | No | The paper mentions using 'Adam W' optimizer but does not specify software dependencies with version numbers, such as Python, PyTorch, or TensorFlow versions. |
| Experiment Setup | Yes | We used the neural network with four layers (including the input layer and output layer), where we used the Re LU for the activation functions and introduced the skip-connection (He et al., 2016) between each hidden layer to make training the network easier. The number of nodes in the hidden layers was fixed to K 3. We used Adam W (Loshchilov and Hutter, 2019) with a learning rate 10 3 and weight decay 10 7 to update the parameters. For training the neural network, we ran Algorithm 3 with N true = 32 and N emp = 90. |