A Robust Approach to Sequential Information Theoretic Planning
Authors: Sue Zheng, Jason Pacheco, John Fisher
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the utility of using robust estimators in the sequential approach on a Gaussian Markov Random Field wherein information measures have a closed form. Lastly, we demonstrate the benefits of our integrated approach in the context of sequential experiment design for inferring causal regulatory networks from gene expression levels. Our method shows improvements over a recent method which selects intervention experiments based on the same MI objective. |
| Researcher Affiliation | Academia | 1Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Boston, MA, USA. Correspondence to: Sue Zheng <szheng@csail.mit.edu>. |
| Pseudocode | No | The paper includes a flow diagram labeled 'Sequential Algorithm' in Figure 2, and describes the algorithm steps via equations (10)-(13), but no structured pseudocode block. |
| Open Source Code | No | No statement about open-sourcing code or a link to a code repository is provided. |
| Open Datasets | No | The paper mentions 'Gaussian MRF' and 'causal gene regulatory networks' and references previous work (Cho et al., 2016) for modeling, but does not provide specific access information (links, DOIs, citations) for the datasets used in experiments. |
| Dataset Splits | No | The paper mentions experimental parameters like number of particles and trials but does not specify train/validation/test splits for any dataset. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | Yes | In our experiments we generate random trees with |V| = 30 nodes, each node having A = 15 randomly generated candidate projection operators. We draw a random sequence of T = 25 nodes to be observed... We compare performance of the sequential robust algorithm against an empirical estimator and naive random selection. Our results are summarized in Fig. 3. ... with M, N = 50 particles. ... at confidence level ϵ = 10 2. |