Batched Gaussian Process Bandit Optimization via Determinantal Point Processes
Authors: Tarun Kathuria, Amit Deshpande, Pushmeet Kohli
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on a variety of synthetic and real-world robotics and hyper-parameter optimization tasks indicate that our DPP-based methods, especially those based on DPP sampling, outperform state-of-the-art methods. |
| Researcher Affiliation | Industry | Tarun Kathuria, Amit Deshpande, Pushmeet Kohli Microsoft Research t-takat@microsoft.com, amitdesh@microsoft.com, pkohli@microsoft.com |
| Pseudocode | Yes | Algorithm 1 GP-BUCB/B-EST Algorithm; Algorithm 2 GP-(UCB/EST)-DPP-(MAX/SAMPLE) Algorithm |
| Open Source Code | No | The paper mentions using publicly available code for baselines (BUCB, PE, EST, GPy Opt) but does not provide a link or statement for their own implementation of the DPP-based methods. |
| Open Datasets | Yes | The Abalone dataset is provided by the UCI Machine Learning Repository at http://archive.ics.uci.edu/ml/datasets/Abalone; Bibtex [15] and Delicious[32] |
| Dataset Splits | No | The paper mentions using synthetic functions and benchmark datasets (Abalone, Bibtex, Delicious) but does not provide specific details on how these datasets were split into training, validation, or test sets for their experiments. |
| Hardware Specification | No | The paper mentions general concepts like 'large parallel processing facilities' or 'multiple experimental setups' but does not specify any concrete hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using publicly available code for baselines and implementing algorithms, but does not provide specific software dependencies with version numbers (e.g., Python version, library versions) for reproducibility. |
| Experiment Setup | Yes | We perform 50 experiments for each objective function and report the median of the immediate regret obtained for each algorithm. To maintain consistency, the first point of all methods is chosen to be the same (random). The mean function of the prior GP was the zero function while the kernel function was the squared-exponential kernel of the form k(x, y) = γ2 exp[ 0.5 P d(xd y2 d)/l2 d]. The hyper-parameter λ was picked from a broad Gaussian hyperprior and the the other hyper-parameters were chosen from uninformative Gamma priors. |