Large-Scale Optimistic Adaptive Submodularity
Authors: Victor Gabillon, Branislav Kveton, Zheng Wen, Brian Eriksson, S. Muthukrishnan
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate our solution on two problems, preference elicitation and face detection, and show that high-quality policies can be learned sample efficiently. ... Lin OASM is evaluated on two real-world problems. The first problem is learning of a policy for movie recommendation. The second problem is learning of an adaptive face detection policy. |
| Researcher Affiliation | Collaboration | Victor Gabillon INRIA Lille team Seque L Villeneuve d Ascq, France victor.gabillon@inria.fr Branislav Kveton Technicolor Labs Palo Alto, CA branislav.kveton@technicolor.com Zheng Wen Electrical Engineering Department Stanford University zhengwen@stanford.edu Brian Eriksson Technicolor Labs Palo Alto, CA brian.eriksson@technicolor.com S. Muthukrishnan Department of Computer Science Rutgers muthu@cs.rutgers.edu |
| Pseudocode | Yes | Algorithm 1 Lin OASM: Optimistic adaptive submodularity with a generalized linear model. |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We experiment with 6k users and 500 most rated movies from the Movie Lens dataset (Lam and Herlocker 2013). ... We experiment with 3k labeled images of faces from the GENKI-SZSL dataset (UCSD 2013). |
| Dataset Splits | No | The paper describes an episodic learning process but does not specify explicit train/validation/test splits of the datasets with percentages or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU, CPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies (e.g., libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | In our experiments, we choose λ = 1 and ρk,t(δ) = 0.1 log(t K + k). |