Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
Authors: Daniel Jarrett, Mihaela Van Der Schaar
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show archetypical examples that exercise our framework through numerical simulation. Examples 1 2 give intuition for optimal active sensing, and 3 5 exemplify potential applications of IAS. Due to space limitation, commentary is necessarily brief; Appendix A gives more context and detail. Figure 4(a) depicts the output of our MAP and MCMC solutions, showing (relevant dimensions of) recovered estimates for optimal softmax strategies, along with the true weights. |
| Researcher Affiliation | Academia | 1Department of Mathematics, University of Cambridge, UK. 2Department of Electrical Engineering, UCLA, USA. Correspondence to: Daniel Jarrett <daniel.jarrett@maths.cam.ac.uk>. |
| Pseudocode | Yes | Algorithm 1 Posterior Sampler for IAS |
| Open Source Code | No | The paper does not provide any explicit statement about releasing code or a link to a code repository. |
| Open Datasets | No | The paper describes using 'numerical simulation' and generating 'episodes' for its examples (e.g., 'N = 300 episodes are simulated'), but does not use or provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper uses numerical simulations for illustrative examples but does not mention specific training, validation, or test splits for any dataset in the context of model evaluation. |
| Hardware Specification | No | The paper mentions 'numerical simulation' but provides no details about the specific hardware (e.g., CPU, GPU models) used to run these simulations. |
| Software Dependencies | No | The paper describes the mathematical framework and algorithms but does not mention any specific software libraries or their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Example 3 (Differential Importance)... we simulate a random collection D of decision episodes for a Bayes-optimal softmax decision agent driven by ηa = (0.25, 0.75). N = 300 episodes are simulated for optimal softmax agents in (a) and the (biased) individual agent in (b), and N = 1000 for the (unbiased) population agent in (b); a greedy lookahead softmax agent (N = 300) is used in (c). Uniform priors P{η| } and P{ρ} are employed in all instances. |