Fundamental Tradeoffs in Learning with Prior Information
Authors: Anirudha Majumdar
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the ability of our framework to provide insights into tradeoffs between prior information and learning performance for problems in estimation, regression, and reinforcement learning. |
| Researcher Affiliation | Academia | Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA. |
| Pseudocode | No | The paper describes mathematical reductions and theoretical tools but does not contain any formally labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., URL, explicit statement of release) for the source code related to its methodology. |
| Open Datasets | Yes | Specifically, we build on the Zipfian Gridworld environments from Chan et al. (2022) |
| Dataset Splits | No | The paper mentions 'training' (e.g., 'For training, the agent receives n environments from Pθ.') but does not specify any training/validation/test dataset splits, percentages, or methodology for partitioning data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions IMPALA (Espeholt et al., 2018) as a method used for training policies but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | No | The paper refers to using a 'pre-computed set A of policies' and training with IMPALA, but it does not provide concrete hyperparameter values or detailed system-level training settings for its experiments. |