Gradient-Based Optimization for Bayesian Preference Elicitation
Authors: Ivan Vendrov, Tyler Lu, Qingqing Huang, Craig Boutilier10292-10301
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our gradientbased EVOI technique achieves state-of-the-art performance across several domains while scaling to large item spaces. |
| Researcher Affiliation | Industry | Ivan Vendrov, Tyler Lu, Qingqing Huang, Craig Boutilier Google Research, Mountain View, California {ivendrov, tylerlu, qqhuang, cboutilier}@google.com |
| Pseudocode | Yes | Algorithm 1 Deep Retr Uniq. Inputs: optimized X and U |
| Open Source Code | No | The paper mentions using "Tensor Flow, Py Torch" as computational frameworks but does not provide any statement about releasing their own source code or a link to a repository. |
| Open Datasets | Yes | Using the Movie Lens 100-K dataset, we train user and movie embeddings with dimension d = 10. ... We use the Movie Lens-20M (Harper and Konstan 2015) dataset and represent each movie with 100 binary attributes from the Tag Genome (Vig, Sen, and Riedl 2012). |
| Dataset Splits | No | The paper uses datasets like Movie Lens 100-K and Goodreads, and describes running elicitation trials with random selections of items and user embeddings, but it does not provide specific train/validation/test dataset split percentages or counts. |
| Hardware Specification | Yes | We benchmark the algorithmic runtimes on a workstation with a 12-core Intel Xeon E5-1650 CPU at 3.6GHz, and 64GB of RAM. |
| Software Dependencies | No | The paper mentions using "Tensor Flow, Py Torch" as computational frameworks but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We initialize query embeddings to random uniform values in [0, 1]100, then run gradient ascent on Eq. 7 for 100 steps, initializing the regularization weight λ at 0.01 and multiplying λ by 1.1 each iteration. |