Risk-Averse Active Sensing for Timely Outcome Prediction under Cost Pressure
Authors: Yuchao Qin, Mihaela van der Schaar, Changhee Lee
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method outperforms baseline active sensing approaches in experiments with both synthetic and realworld datasets, and we illustrate the significance of our policy decomposition and the necessity of a risk-averse sensing policy through case studies. In the experiments, we evaluate the effectiveness of our proposed risk-averse active sensing approach RAS on both synthetic and real-world healthcare datasets. |
| Researcher Affiliation | Academia | Yuchao Qin Univerisity of Cambridge, UK Mihaela van der Schaar University of Cambridge, UK Alan Turing Institute, UK Changhee Lee Chung-Ang University, South Korea |
| Pseudocode | Yes | Algorithm 1 Risk-averse active sensing |
| Open Source Code | Yes | The source code of RAS can be found in the two Git Hub repositories listed below: The van der Schaar lab repo: https://github.com/vanderschaarlab/cvar_sensing The author s personal repo: https://github.com/yvchao/cvar_sensing |
| Open Datasets | Yes | ADNI dataset. The Alzheimer s Disease Neuroimaging Initiative2 (ADNI) dataset includes records on AD progression of N = 1002 patients with regular follow-ups every six months.2https://adni.loni.usc.edu |
| Dataset Splits | Yes | We first fit the outcome estimator f P on each dataset with 64/16/20 train/validation/test splits |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'neural CDE' and 'Python package torchcde for interpolation' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In our experiments, the hyperparameters of each active sensing method are selected based on the cost efficiency, i.e., ROC / Cost, obtained on the test set. For the synthetic data considered in our manuscript, we set the minimum and maximum allowed acquisition intervals as min = 0.2, max = 1.0, respectively. ... RAS: the coefficient for diagnostic error λ = 300 {200, 250, 280, 300, 310, 320, 350, 400}, discount factor γ = 0.99, tail-risk quantile α = 0.1, penalty for invalid visits ν = 10. All methods are trained with K = 200 iterations in the experiments. For RAS, we set the tail subset update interval M = 10 for Algorithm 1 in the manuscript. |