EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Authors: Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez-Lobato, Sebastian Nowozin, Cheng Zhang
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate EDDI on two different tasks within information search: news recommendation and scientific paper search. Our results demonstrate that EDDI learns to identify high-value articles that generate user engagement, outperforming existing baselines by up to 20% on click-through rate, and also to find relevant, impactful scientific papers, achieving a 15% improvement in F1-score over existing methods. |
| Researcher Affiliation | Academia | University of Cambridge, Google Research, University College London |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide an explicit link to open-source code or state that the code is publicly available. |
| Open Datasets | Yes | We evaluate EDDI on two different tasks within information search: news recommendation and scientific paper search. For news recommendation, we use the MIND (Microsoft News Dataset) [33], which is a large-scale real-world dataset comprising news articles and user click logs collected from Microsoft News. For scientific paper search, we use the DBLP-Citation dataset [32], which contains metadata of scientific papers and their citation relationships. |
| Dataset Splits | Yes | We use the provided training and validation sets for both MIND and DBLP-Citation datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper mentions software components like PyTorch but does not provide specific version numbers for them or any other relevant libraries/solvers. |
| Experiment Setup | Yes | We train EDDI using Adam optimizer [25] with a learning rate of 1e-4 and a batch size of 64. The model is trained for 10 epochs. We use a latent dimension of 64 for the encoder and decoder. The temperature parameter τ is set to 0.1. For the prior network, we use a two-layer feed-forward network with ReLU activation. The dimension of hidden layers for the prior network is 128. For the inference network, we use a two-layer feed-forward network with ReLU activation, and the dimension of hidden layers is 128. |