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 [1].
Strategic Apple Tasting
Authors: Keegan Harris, Chara Podimata, Steven Z. Wu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a learning algorithm which incurs O( T) strategic regret when the sequence of agents is chosen stochastically. We also give an algorithm capable of handling adversarially-chosen agents, albeit at the cost of O(T (d+1)/(d+2)) strategic regret (where d is the dimension of the context). |
| Researcher Affiliation | Academia | Keegan Harris Carnegie Mellon University Pittsburgh, PA 15213 EMAIL Chara Podimata MIT & Archimedes/Athena RC Cambridge, MA 02142 EMAIL Zhiwei Steven Wu Carnegie Mellon University Pittsburgh, PA 15213 EMAIL |
| Pseudocode | Yes | ALGORITHM 1: Strategy-Aware OLS with Apple Tasting Feedback (SA-OLS) |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not refer to any specific dataset, public or otherwise. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training settings. |