data efficiency

benchmarking.qmd

@@ -776,13 +776,13 @@ There are several approaches that can be taken to improve data quality. These me
 
 -   Data Cleaning: This involves handling missing values, correcting errors, and removing outliers. Clean data ensures that the model is not learning from noise or inaccuracies.
 
--   Data Interpretability and Explainability: Common techniques include [[LIME]{.underline}](https://arxiv.org/abs/1602.04938) which provides insight into the decision boundaries of classifiers, and [[Shapley values]{.underline}](https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf) which estimate the importance of individual samples in contributing to a model's predictions.
+-   Data Interpretability and Explainability: Common techniques include LIME [@ribeiro2016should] which provides insight into the decision boundaries of classifiers, and Shapley values [@lundberg2017unified] which estimate the importance of individual samples in contributing to a model's predictions.
 
 -   Feature Engineering: Transforming or creating new features can significantly improve model performance by providing more relevant information for learning.
 
 -   Data Augmentation: Augmenting data by creating new samples through various transformations can help improve model robustness and generalization.
 
--   Active Learning: This is a semi-supervised learning approach where the model actively queries a human oracle to label the most informative samples [[[Coleman et al, 2020]{.underline}](https://arxiv.org/abs/2007.00077)]. This ensures that the model is trained on the most relevant data.
+-   Active Learning: This is a semi-supervised learning approach where the model actively queries a human oracle to label the most informative samples [@coleman2022similarity]. This ensures that the model is trained on the most relevant data.
 
 -   Dimensionality Reduction: Techniques like PCA can be used to reduce the number of features in a dataset, thereby reducing complexity and training time.
 

references.bib

@@ -540,4 +540,36 @@ @article{xu2023demystifying
   author={Xu, Hu and Xie, Saining and Tan, Xiaoqing Ellen and Huang, Po-Yao and Howes, Russell and Sharma, Vasu and Li, Shang-Wen and Ghosh, Gargi and Zettlemoyer, Luke and Feichtenhofer, Christoph},
   journal={arXiv preprint arXiv:2309.16671},
   year={2023}
+}
+@inproceedings{coleman2022similarity,
+  title={Similarity search for efficient active learning and search of rare concepts},
+  author={Coleman, Cody and Chou, Edward and Katz-Samuels, Julian and Culatana, Sean and Bailis, Peter and Berg, Alexander C and Nowak, Robert and Sumbaly, Roshan and Zaharia, Matei and Yalniz, I Zeki},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={36},
+  number={6},
+  pages={6402--6410},
+  year={2022}
+}
+@inproceedings{ribeiro2016should,
+  title={" Why should i trust you?" Explaining the predictions of any classifier},
+  author={Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
+  booktitle={Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining},
+  pages={1135--1144},
+  year={2016}
+}
+@article{lundberg2017unified,
+  title={A unified approach to interpreting model predictions},
+  author={Lundberg, Scott M and Lee, Su-In},
+  journal={Advances in neural information processing systems},
+  volume={30},
+  year={2017}
+}
+@inproceedings{coleman2022similarity,
+  title={Similarity search for efficient active learning and search of rare concepts},
+  author={Coleman, Cody and Chou, Edward and Katz-Samuels, Julian and Culatana, Sean and Bailis, Peter and Berg, Alexander C and Nowak, Robert and Sumbaly, Roshan and Zaharia, Matei and Yalniz, I Zeki},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={36},
+  number={6},
+  pages={6402--6410},
+  year={2022}
 }