Submodular Surrogates for Value of Information
Authors: Yuxin Chen, Shervin Javdani, Amin Karbasi, J. Bagnell, Siddhartha Srinivasa, Andreas Krause
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the utility of our approach on four diverse case-studies: touch-based robotic localization, comparison-based preference learning, wild-life conservation management, and preference elicitation in behavioral economics. In the first application, we demonstrate DIRECT in closed-loop on an actual robotic platform. Experimental results show that our algorithm significantly outperforms myopic value of information in most settings. |
| Researcher Affiliation | Academia | Yuxin Chen ETH Z urich yuxin.chen@inf.ethz.ch Shervin Javdani Carnegie Mellon University sjavdani@cmu.edu Amin Karbasi Yale University amin.karbasi@yale.edu J. Andrew Bagnell Carnegie Mellon University dbagnell@ri.cmu.edu Siddhartha Srinivasa Carnegie Mellon University ss5@andrew.cmu.edu Andreas Krause ETH Z urich krausea@ethz.ch |
| Pseudocode | No | The paper describes algorithmic steps in narrative text and mathematical formulations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing the source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We use the Movie Lens 100k dataset (Herlocker et al. 1999), which consists a matrix of 1 to 5 ratings of 1682 movies from 943 users. |
| Dataset Splits | No | The paper uses datasets but does not explicitly state specific training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper mentions running experiments on a 'real robot platform' and 'simulated data' but does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper references parameters from previous works for specific applications (e.g., 'adopt the same parameters as reported in (Javdani et al. 2014)', 'employ the same set of parameters used in (Ray et al. 2012)') but does not explicitly list concrete hyperparameter values, training configurations, or system-level settings for the DIRECT algorithm in its main text. |