| A unified approach to quantifying algorithmic unfairness: Measuring individual &group unfairness via inequality indices T Speicher, H Heidari, N Grgic-Hlaca, KP Gummadi, A Singla, A Weller, ... Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 267 | 2018 |
| Potential for discrimination in online targeted advertising T Speicher, M Ali, G Venkatadri, FN Ribeiro, G Arvanitakis, F Benevenuto, ... Conference on fairness, accountability and transparency, 5-19, 2018 | 196 | 2018 |
| A generalized language model as the combination of skipped n-grams and modified Kneser-Ney smoothing R Pickhardt, T Gottron, M Körner, PG Wagner, T Speicher, S Staab arXiv preprint arXiv:1404.3377, 2014 | 37 | 2014 |
| Measuring Representational Robustness of Neural Networks Through Shared Invariances V Nanda, T Speicher, C Kolling, JP Dickerson, K Gummadi, A Weller International Conference on Machine Learning, 16368-16382, 2022 | 3 | 2022 |
| Reliable learning by subsuming a trusted model: Safe exploration of the space of complex models T Speicher, MB Zafar, KP Gummadi, A Singla, A Weller Proc. Int. Conf. Mach. Learn. Workshop (ICML), 1-5, 2017 | 1 | 2017 |
| Diffused Redundancy in Pre-trained Representations V Nanda, T Speicher, JP Dickerson, S Feizi, KP Gummadi, A Weller arXiv preprint arXiv:2306.00183, 2023 | | 2023 |
| Pointwise Representational Similarity C Kolling, T Speicher, V Nanda, M Toneva, KP Gummadi arXiv preprint arXiv:2305.19294, 2023 | | 2023 |
| Learned Neural Network Representations are Spread Diffusely with Redundancy V Nanda, T Speicher, JP Dickerson, S Feizi, K Gummadi, A Weller | | 2022 |
| Invariance Makes a Difference: Disentangling the Role of Invariance and Equivariance in Representations T Speicher, V Nanda, KP Gummadi | | 2022 |
| Unifying Model Explainability and Robustness via Machine-Checkable Concepts V Nanda, T Speicher, JP Dickerson, KP Gummadi, MB Zafar arXiv preprint arXiv:2007.00251, 2020 | | 2020 |
