Structured Learning of Compositional Sequential Interventions
Authors: Jialin Yu, Andreas Koukorinis, Nicolo Colombo, Yuchen Zhu, Ricardo Silva
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run a number of synthetic and semi-synthetic experiments to evaluate the performance of the CSI-VAE approach. In this section, we summarize our experimental set-up and main results. |
| Researcher Affiliation | Academia | Jialin Yu1 Andreas Koukorinis1 Nicolò Colombo2 Yuchen Zhu1 Ricardo Silva1 1University College London 2Royal Holloway, University of London |
| Pseudocode | No | The paper describes the algorithm (CSI-VAE) in prose within Section 3 ('Algorithm and Statistical Inference' and '3.1 Algorithm: CSI-VAE') but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | Yes | The code for reproducing all results and figures is available online2. 2The code is available at https://github.com/jialin-yu/CSI-VAE |
| Open Datasets | Yes | For simulator (2), we construct simulated datasets of size 3, 000, again containing 5 different interventions, an initial period T0 = 25, a maximum of 3 different interventions per unit, and r = 10. The task is to predict outcomes for interventions not applied yet within any given unit (i.e., at least from the 2 options left). In simulator (2), parameters ϕ and β are learned from real-world data. Interventions are artificial, but inspired by the process of showing different proportions of track types to an user in a Spotify-like environment. ...The dataset comes from Spotify5, which is an online music streaming platform... For a detailed description of the dataset, please refer to [10]. Reference [10] is: B. Brost, R. Mehrotra, and T. Jehan. The music streaming sessions dataset. In The World Wide Web Conference, pages 2594 2600, 2019. |
| Dataset Splits | Yes | For both setups, we use a data ratio of 0.7, 0.1, 0.2 for training, validation and test, respectively. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like Adam optimizer, GRU, LSTM, and Transformer models, but it does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Each experiment was repeated 5 times, using Adam [26] at a learning rate of 0.01, with 50 epochs in the fully-synthetic case and 100 for the semi-synthetic, which was enough for convergence. We selected the best iteration point based on a small holdout set. |