Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fundamental Tradeoffs in Learning with Prior Information
Authors: Anirudha Majumdar
ICML 2023 | Venue PDF | 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. |