## 2023
- Carsten Lüth, Till Bungert, Lukas Klein, and Paul Jaeger. Navigating the pitfalls of active learning evaluation: A systematic framework for meaningful performance assessment. Advances in Neural Information Processing Systems, 36, 2024.
- Yilin Ji, Daniel Kaestner, Oliver Wirth, and Christian Wressnegger. Randomness is the root of all evil: More reliable evaluation of deep active learning. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3943–3952, 2023.

## 2022
- Guy Hacohen, Avihu Dekel, and Daphna Weinshall. Active learning on a budget: Opposite strategies suit high and low budgets. arXiv preprint arXiv:2202.02794, 2022.
- Jifan Zhang, Julian Katz-Samuels, and Robert Nowak. Galaxy: Graph-based active learning at the extreme. In International Conference on Machine Learning, pages 26223–26238. PMLR, 2022.
- Mussmann, S., Reisler, J., Tsai, D., Mousavi, E., O'Brien, S., & Goldszmidt, M. (2022). Active learning with expected error reduction. arXiv preprint arXiv:2211.09283.

## 2021
- Razvan Caramalau, Binod Bhattarai, and Tae-Kyun Kim. Sequential graph convolutional network for active learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9583–9592, 2021.

## 2020
- Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. Deep batch active learning by diverse, uncertain gradient lower bounds. In International Conference on Learning Representations, 2020.

## 2019
- Gissin, Daniel, and Shai Shalev-Shwartz. "Discriminative active learning." arXiv preprint arXiv:1907.06347 (2019).
- Jinhan Kim, Robert Feldt, and Shin Yoo. Guiding deep learning system testing using surprise adequacy. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), pages 1039–1049. IEEE, 2019.

## 2018
- Ducoffe, Melanie, and Frederic Precioso. "Adversarial active learning for deep networks: a margin based approach." arXiv preprint arXiv:1802.09841 (2018).
- Beluch, William H., et al. "The power of ensembles for active learning in image classification." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.

## 2017
- Andreas Kirsch, Joost Van Amersfoort, and Yarin Gal. Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning. Advances in neural information processing systems, 32, 2019.
- Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-set approach. arXiv preprint arXiv:1708.00489, 2017.

## Older
- Guo, Yuhong, and Russell Greiner. "Optimistic active-learning using mutual information." IJCAI. Vol. 7. 2007.
- Nguyen, Hieu T., and Arnold Smeulders. "Active learning using pre-clustering." Proceedings of the twenty-first international conference on Machine learning. 2004.

The topic of this seminar is Active Learning.
For an overview of what Active Learning is, please refer to https://burrsettles.com/pub/settles.activelearning.pdf#page=14.10

MSc WI & IMIT & AINF & DA -  MSc 1-3