| xview: Objects in context in overhead imagery. D Lam, R Kuzma, K McGee, S Dooley, M Laielli, M Klaric, Y Bulatov, ... arXiv preprint arXiv:1802.07856, 2018 | 396 | 2018 |
| Fairness through robustness: Investigating robustness disparity in deep learning V Nanda, S Dooley, S Singla, S Feizi, JP Dickerson Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021 | 112 | 2021 |
| Smaug: Fixing failure modes of preference optimisation with dpo-positive A Pal, D Karkhanis, S Dooley, M Roberts, S Naidu, C White arXiv preprint arXiv:2402.13228, 2024 | 97 | 2024 |
| ForecastPFN: Synthetically-Trained Zero-Shot Forecasting S Dooley, GS Khurana, C Mohapatra, S Naidu, C White Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 62 | 2023 |
| Livebench: A challenging, contamination-free llm benchmark C White, S Dooley, M Roberts, A Pal, B Feuer, S Jain, R Shwartz-Ziv, ... arXiv preprint arXiv:2406.19314, 2024 | 49 | 2024 |
| To the cutoff... and beyond? a longitudinal perspective on LLM data contamination M Roberts, H Thakur, C Herlihy, C White, S Dooley The Twelfth International Conference on Learning Representations, 2024 | 44* | 2024 |
| Preferencenet: Encoding human preferences in auction design with deep learning N Peri, M Curry, S Dooley, J Dickerson Advances in Neural Information Processing Systems 34, 17532-17542, 2021 | 38 | 2021 |
| Proportionnet: Balancing fairness and revenue for auction design with deep learning K Kuo, A Ostuni, E Horishny, MJ Curry, S Dooley, P Chiang, T Goldstein, ... arXiv preprint arXiv:2010.06398, 2020 | 35 | 2020 |
| Giraffe: Adventures in expanding context lengths in llms A Pal, D Karkhanis, M Roberts, S Dooley, A Sundararajan, S Naidu arXiv preprint arXiv:2308.10882, 2023 | 34 | 2023 |
| Rethinking bias mitigation: Fairer architectures make for fairer face recognition S Dooley, R Sukthanker, J Dickerson, C White, F Hutter, M Goldblum Advances in Neural Information Processing Systems 36, 74366-74393, 2023 | 29* | 2023 |
| Field evidence of the effects of privacy, data transparency, and pro-social appeals on COVID-19 app attractiveness S Dooley, D Turjeman, JP Dickerson, EM Redmiles Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems …, 2022 | 21 | 2022 |
| A deep dive into dataset imbalance and bias in face identification V Cherepanova, S Reich, S Dooley, H Souri, J Dickerson, M Goldblum, ... Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 229-247, 2023 | 20 | 2023 |
| Comparing human and machine bias in face recognition S Dooley, R Downing, G Wei, N Shankar, B Thymes, G Thorkelsdottir, ... arXiv preprint arXiv:2110.08396, 2021 | 19 | 2021 |
| Robustness disparities in commercial face detection S Dooley, T Goldstein, JP Dickerson arXiv preprint arXiv:2108.12508, 2021 | 19 | 2021 |
| Large Language Models Must Be Taught to Know What They Don't Know S Kapoor, N Gruver, M Roberts, K Collins, A Pal, U Bhatt, A Weller, ... arXiv preprint arXiv:2406.08391, 2024 | 14 | 2024 |
| Robustness disparities in face detection S Dooley, GZ Wei, T Goldstein, J Dickerson Advances in Neural Information Processing Systems 35, 38245-38259, 2022 | 14 | 2022 |
| Calibration-tuning: Teaching large language models to know what they don’t know S Kapoor, N Gruver, M Roberts, A Pal, S Dooley, M Goldblum, A Wilson Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024 …, 2024 | 11 | 2024 |
| Overhead detection: Beyond 8-bits and rgb E Mace, K Manville, M Barbu-McInnis, M Laielli, M Klaric, S Dooley arXiv preprint arXiv:1808.02443, 2018 | 10 | 2018 |
| { How} Library {IT} Staff Navigate Privacy and Security Challenges and Responsibilities AF Luo, N Warford, S Dooley, R Greenstadt, ML Mazurek, N McDonald 32nd USENIX Security Symposium (USENIX Security 23), 5647-5664, 2023 | 9 | 2023 |
| Are commercial face detection models as biased as academic models? S Dooley, GZ Wei, T Goldstein, JP Dickerson arXiv preprint arXiv:2201.10047, 2022 | 8 | 2022 |
