Learning from Streaming Data when Users Choose
Authors: Jinyan Su, Sarah Dean
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also experimentally demonstrate the utility of our algorithm with real world data. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Cornell University. Correspondence to: Jinyan Su <js3673@cornell.edu>, Sarah Dean <sdean@cornell.edu>. |
| Pseudocode | Yes | Algorithm 1 Multi-learner Streaming Gradient Descent (MSGD) |
| Open Source Code | Yes | Code for reproducing these results can be found at https://github.com/ sdean-group/MSGD. |
| Open Datasets | Yes | Our first experimental setting is based on a widely used movie recommendation dataset Movielens10M (Harper & Konstan, 2015)... Our second setting is based on census data made available by folktables (Ding et al., 2021). |
| Dataset Splits | No | The paper mentions splitting data for testing ('0.2 of them for testing') but does not explicitly state a validation set or describe a train/validation/test split. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., specific GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper mentions 'Python toolkit Surprise' and 'folktables' but does not provide specific version numbers for these software components or for Python itself. |
| Experiment Setup | Yes | Then the selected service updates their parameter with the gradient of the loss on the user s data with step size ηt = 1/t. At each time step, we sample a user x at random from the data described above. We assign this user to one of k services according to bounded rational with parameters ζ. For MSGD, we illustrate results of different total number of services k = 2, 4, 6. for each k, we use compute the average accuracy after T = 2000 k total timesteps and plot the average over 3 trials. |