Teaching an Active Learner with Contrastive Examples
Authors: Chaoqi Wang, Adish Singla, Yuxin Chen
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Numerical Experiments. We conducted experiments on two datasets |
| Researcher Affiliation | Academia | Chaoqi Wang University of Chicago chaoqi@uchicago.edu Adish Singla MPI-SWS adishs@mpi-sws.org Yuxin Chen University of Chicago chenyuxin@uchicago.edu |
| Pseudocode | Yes | Algorithm 2 Greedy teaching algorithm for active leaner |
| Open Source Code | No | The paper does not include any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We conducted experiments on two datasets: Vespula-Weevil and Butterfly-Moth, which were adopted by Singla et al. [2013, 2014] for verifying the algorithm for teaching the crowd to classify images. |
| Dataset Splits | No | The paper describes the datasets and how they were used, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | 5.1 Experimental Setup: β-greedy active learners: In the experiments, we consider the class of β-greedy active learners, for which the selected query xq t+1 satisfies ... We can interpolate β from 1 to + to cover a broad range of active learners with different properties. ... In terms of the constraint on the teacher’s example, we consider the following three types of the constraints: 1) examples that are close to the learner’s query but with different label (denoted by C); 2) examples that are far away from the learner’s query but with the same label(denoted by F); 3) the union of 1)& 2) (denoted by C+F). For each constraint, we also adopt a parameter ψ [0, 1] to control the size of the search space... |