Metric-Fair Active Learning
Authors: Jie Shen, Nan Cui, Jing Wang
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we henceforth study metric-fair active learning of homogeneous halfspaces, and show that under the distribution-dependent PAC learning model, fairness and label efficiency can be achieved simultaneously. We further propose two extensions of our main results: 1) we show that it is possible to make the algorithm robust to the adversarial noise one of the most challenging noise models in learning theory; and 2) it is possible to significantly improve the label complexity when the underlying halfspace is sparse. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA. 2Amazon, New York City, New York, USA. |
| Pseudocode | Yes | Algorithm 1 Active Learning of Halfspaces with Approximate Metric-Fairness ... Algorithm 2 Active Learning of Sparse Halfspaces with Approximate Metric-Fairness |
| Open Source Code | No | The paper does not include any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on a 'distribution-dependent PAC learning model' and properties of 'marginal distribution DX' or 'distribution D', rather than using a specific publicly available dataset for empirical training. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithmic design and proofs, without performing empirical experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe computational experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | Yes | Our algorithms proceed in phases. In each phase k, we set rk = 2^(k-3), bk = c rk, ρk = 2t rk, τk = κ bk, ... We will draw a set of instances Tk by calling EXD for nk times, where nk = K1 / (alpha^2) (t log^4 d / (delta_k)) ... We will then query the label of some instances in Tk by calling EXY D for mk times, where mk = K2 t log^3 d / (alpha*delta_k) log d. ... Algorithm 1 ... Require: Target classification error rate ϵ, failure probability δ, fairness metric function ζ( , ), fairness error rate α |