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].
Online Allocation and Learning in the Presence of Strategic Agents
Authors: Steven Yin, Shipra Agrawal, Assaf Zeevi
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present an online allocation mechanism that is approximately-BIC, and further achieves low regret guarantees on individual regret when all agents are truthful. Our algorithm contains two components: a learner, and a detector. ... Our main results are the following guarantees on incentive compatibility and regret of our online allocation algorithm. Theorem 1 (Approximate-BIC). Algorithm 1 is (O(n T log(n T/δ)), δ)-approximate BIC. ... Theorem 2 (Individual Regret). ... All the missing details of the proofs are provided in the appendix. Finally, in Section 7 we discuss some limitations and future directions. |
| Researcher Affiliation | Academia | Steven Yin Department of Industrial Engineering and Operations Research Columbia University New York, NY 10027 EMAIL Shipra Agrawal Department of Industrial Engineering and Operations Research Columbia University New York, NY 10027 EMAIL Assaf Zeevi Graduate School of Business Columbia University New York, NY 10027 EMAIL |
| Pseudocode | Yes | Algorithm 1: Epoch Based Online Allocation Algorithm Algorithm 2: Detection Algorithm |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training. The checklist explicitly states N/A for experimental results. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation splits. The checklist explicitly states N/A for experimental results. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require hardware specifications. The checklist explicitly states N/A for experimental results. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments that would require software dependencies. The checklist explicitly states N/A for experimental results. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. The checklist explicitly states N/A for experimental results. |