Classification with Few Tests through Self-Selection
Authors: Hanrui Zhang, Yu Cheng, Vincent Conitzer5805-5812
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper consistently presents lemmas, theorems, propositions, and definitions, and uses examples for illustration, indicating a focus on conceptual and mathematical contributions rather than empirical validation. For instance, in Section 3, it states: "Lemma 1. Fixing a policy P and a cost per test c, the optimal expected reward of any agent is achieved by one of the following two strategies:..." and in Section 4.2: "Theorem 2. Let P O = [k] be a set of outcomes such that P argmax S O:G(S)>B(S) G(S). P = {{o} | o P} {{o1, o2} | o1, o2 O} is perfectly implementable..." |
| Researcher Affiliation | Academia | Hanrui Zhang,1 Yu Cheng, 2 Vincent Conitzer 1 1 Duke University 2 University of Illinois at Chicago hrzhang@cs.duke.edu, yucheng2@uic.edu, conitzer@cs.duke.edu |
| Pseudocode | No | The paper presents lemmas, theorems, and definitions, along with prose descriptions of agents' strategies and policies, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing source code, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper is theoretical and does not mention using any datasets for training or evaluation, nor does it provide access information for any data. It uses abstract distributions G and B. |
| Dataset Splits | No | The paper is theoretical and does not involve data splits for training, validation, or testing. The discussion is based on mathematical models of distributions. |
| Hardware Specification | No | The paper is purely theoretical and does not mention any hardware specifications used for running experiments. |
| Software Dependencies | No | The paper is purely theoretical and does not mention any software dependencies or specific versions for replication, as no computational experiments are described. |
| Experiment Setup | No | The paper describes a theoretical framework and proves properties of policies and agent behavior. It does not include details about an experimental setup, hyperparameters, or training configurations. |