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..
Bayesian decision-making under misspecified priors with applications to meta-learning
Authors: Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miro Dudik, Robert E. Schapire
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through numerical simulations, we illustrate how prior misspecification and the deployment of one-step look-ahead (as in KG) can impact the convergence of meta-learning in multi-armed and contextual bandits with structured and correlated priors. |
| Researcher Affiliation | Collaboration | Massachusetts Institute of Technology, EMAIL Columbia University Microsoft Research NYC Massachusetts Institute of Technology |
| Pseudocode | No | The paper describes algorithms in text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions using synthetic data for experiments (e.g., 'synthetic experiments in multi-armed and contextual bandit settings', 'synthetic linear contextual bandit problem') but does not provide access information (link, DOI, or citation) for these datasets, nor does it refer to well-known public datasets. |
| Dataset Splits | No | The paper describes its experimental setup and meta-learning approach but does not specify dataset splits (e.g., percentages or counts) for training, validation, or testing. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Our first scenario is a multi-armed bandit problem with Gaussian prior and Gaussian rewards. The instance has |A| = 6 arms and each episode has horizon H = 10. The prior is N(ν , Ψ ) where ν = [0.5, 0, 0, 0.1, 0, 0] and Ψ has block structure... The rewards are Gaussian with variance 1... Both meta-learners are run in an explore-then-commit fashion where the first T0 episodes are used for exploration. |