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..
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. |