When to Make and Break Commitments?
Authors: Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically evaluate the performance of our algorithm in running clinical trials with subpopulation selection. |
| Researcher Affiliation | Academia | Alihan H uy uk University of Cambridge ah2075@cam.ac.uk Zhaozhi Qian University of Cambridge zq224@cam.ac.uk Mihaela van der Schaar University of Cambridge The Alan Turing Institute mv472@cam.ac.uk |
| Pseudocode | Yes | Our algorithm is called Bayes-OCP and is given in Algorithm 1. |
| Open Source Code | Yes | Moreover, the source code necessary to reproduce our main results in Table 3 is made publicly available at https://github.com/alihanhyk/optcommit and https://github.com/vanderschaarlab/optcommit. |
| Open Datasets | No | To this end, we randomly generated 1000 environments (repeated five times to obtain error bars) with true mean outcomes θXA, θXB sampled independently from N(0.1, 0.1). This indicates a simulated dataset, not a publicly available fixed dataset with access information. |
| Dataset Splits | No | The paper describes simulating environments and evaluating algorithms on these simulations, but it does not specify train/validation/test splits of a pre-existing dataset, nor does it provide a methodology for creating reproducible splits if the simulated data were to be saved and reused. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, cloud instance types) used to run the experiments or simulations. |
| Software Dependencies | No | The paper does not specify versions for any software dependencies, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | In our environments, there are two atomic-populations, X = {XA, XB}. Both atomicpopulations have equal propensities ηXA = ηXB = 1/2 and the meta-experimenter has the same positively-biased prior for the mean outcome associated with each atomic-population: θXA, θXB N(0.1, 0.1). Experiment designs targeting one or both of these atomic-populations all have the same time horizon τ = 600 and success criterion ρ(Dτ) = 1{P (xt,yt) Dτ yt/|Dτ| > α/ τ}, where α = F 1(95%). Rewards are given by RX = 1000η0.1 X and costs are given by CX = 1/η0.1 X. Bayes-OCP is initialized with β = 0.80. |