Recovering Bandits
Authors: Ciara Pike-Burke, Steffen Grunewalder
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement these discussions with regret bounds and empirical studies. We show empirical results in Section 7 |
| Researcher Affiliation | Academia | Ciara Pike-Burke Universitat Pompeu Fabra Barcelona, Spain c.pikeburke@gmail.com; Steffen Grünewälder Lancaster University Lancaster, UK s.grunewalder@lancaster.ac.uk |
| Pseudocode | Yes | Algorithm 1 d-step lookahead UCB and Thompson Sampling |
| Open Source Code | No | The paper references 'GPy: A gaussian process framework in python. http://github.com/Sheffield ML/GPy, 2012 .' which is a third-party library used for fitting GPs, not the source code for the authors' proposed algorithms. There is no other statement regarding the release of their code. |
| Open Datasets | No | The paper describes synthetic data generation: 'We tested our algorithms in experiments with zmax = 30, noise standard deviation σ = 0.1, and horizon T = 1000.' and 'sampled the recovery functions from a squared exponential kernel' or 'The recovery functions were logistic... and modified gamma'. No public dataset or access information is provided. |
| Dataset Splits | No | The paper describes the experimental setup including parameters like 'zmax = 30, noise standard deviation σ = 0.1, and horizon T = 1000.' and the number of replications, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'We used GPy [11] to fit the GPs.' and provides a reference to 'GPy: A gaussian process framework in python. http://github.com/Sheffield ML/GPy, 2012 .', but it does not specify a version number for GPy or any other software dependencies like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | We tested our algorithms in experiments with zmax = 30, noise standard deviation σ = 0.1, and horizon T = 1000. We averaged all results over 100 replications and used a squared exponential kernel with l = 4. We used squared exponential kernels in 1RGP-UCB and 1RGP-TS with lengthscale l = 5. The recovery functions were logistic, f(z) = θ0(1 + exp{ θ1(z θ2)}) 1 and modified gamma, f(z) = θ0C exp{ θ1z}zθ2 |