Provable Pathways: Learning Multiple Tasks over Multiple Paths
Authors: Yingcong Li, Samet Oymak
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments support our theory and verify the benefits of multipath representations. Finally, we also highlight multiple future directions. |
| Researcher Affiliation | Academia | 1 University of California, Riverside, 2 University of Michigan, Ann Arbor {yli692@, oymak@ece.}ucr.edu |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper describes generating synthetic data for experiments (e.g., "generate B1 and {Bk 2}K k=1 with orthonormal rows uniformly at random independently.") but does not provide access information for the generated dataset or the code to reproduce its generation. |
| Dataset Splits | No | The paper specifies 'N' samples per task for MTL training and 'M' samples for transfer learning, but it does not explicitly detail training, validation, and test splits for a single dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers. |
| Experiment Setup | Yes | We set ambient dimension p = 32, shared embedding R = 8, and cluster embeddings r = 2. We consider a base configuration of K = 40 clusters, T = T/K = 10 tasks per cluster and N = 10 samples per task (see supplementary material for further details). ... In the experiment, we set γ = 0.6 to make sure hindsight knowledge of θ t is sufficient to correctly cluster all tasks. |