| Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Foundations and Trends® in Machine Learning 14 (1–2), 1-210, 2021 | 1947 | 2021 |
| Examples are not enough, learn to criticize! criticism for interpretability B Kim, R Khanna, OO Koyejo Advances in Neural Information Processing Systems, 2280-2288, 2016 | 614 | 2016 |
| The dynamics of functional brain networks: integrated network states during cognitive task performance JM Shine, PG Bissett, PT Bell, O Koyejo, JH Balsters, KJ Gorgolewski, ... Neuron 92 (2), 544-554, 2016 | 516 | 2016 |
| Toward open sharing of task-based fMRI data: the OpenfMRI project RA Poldrack, DM Barch, JP Mitchell, TD Wager, AD Wagner, JT Devlin, ... Frontiers in neuroinformatics 7, 12, 2013 | 327 | 2013 |
| Asynchronous federated optimization C Xie, S Koyejo, I Gupta arXiv preprint arXiv:1903.03934, 2019 | 195 | 2019 |
| Consistent binary classification with generalized performance metrics OO Koyejo, N Natarajan, PK Ravikumar, IS Dhillon Advances in neural information processing systems 27, 2014 | 153 | 2014 |
| Generalized byzantine-tolerant sgd C Xie, O Koyejo, I Gupta arXiv preprint arXiv:1802.10116, 2018 | 140 | 2018 |
| Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives JM Shine, O Koyejo, PT Bell, KJ Gorgolewski, M Gilat, RA Poldrack NeuroImage 122, 399-407, 2015 | 130 | 2015 |
| Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance C Xie, S Koyejo, I Gupta International Conference on Machine Learning, 6893-6901, 2019 | 108 | 2019 |
| Max-sliced wasserstein distance and its use for gans I Deshpande, YT Hu, R Sun, A Pyrros, N Siddiqui, S Koyejo, Z Zhao, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 87 | 2019 |
| A field guide to federated optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ... arXiv preprint arXiv:2107.06917, 2021 | 84 | 2021 |
| Towards realistic individual recourse and actionable explanations in black-box decision making systems S Joshi, O Koyejo, W Vijitbenjaronk, B Kim, J Ghosh arXiv preprint arXiv:1907.09615, 2019 | 81 | 2019 |
| Fall of empires: Breaking byzantine-tolerant sgd by inner product manipulation C Xie, O Koyejo, I Gupta Uncertainty in Artificial Intelligence, 261-270, 2020 | 79 | 2020 |
| Interpreting black box predictions using fisher kernels R Khanna, B Kim, J Ghosh, S Koyejo The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 66 | 2019 |
| False discovery rate smoothing W Tansey, O Koyejo, RA Poldrack, JG Scott Journal of the American Statistical Association 113 (523), 1156-1171, 2018 | 44* | 2018 |
| Zeno: Byzantine-suspicious stochastic gradient descent C Xie, O Koyejo, I Gupta arXiv preprint arXiv:1805.10032 24, 2018 | 43 | 2018 |
| Zeno++: Robust fully asynchronous SGD C Xie, S Koyejo, I Gupta International Conference on Machine Learning, 10495-10503, 2020 | 40 | 2020 |
| Phocas: dimensional byzantine-resilient stochastic gradient descent C Xie, O Koyejo, I Gupta arXiv preprint arXiv:1805.09682, 2018 | 39 | 2018 |
| Advances and open problems in federated learning. arXiv 2019 P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... arXiv preprint arXiv:1912.04977, 1912 | 37 | 1912 |
| SLSGD: Secure and efficient distributed on-device machine learning C Xie, O Koyejo, I Gupta Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019 | 30 | 2019 |
Russ PoldrackProfessor of Psychology, Stanford UniversityΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα stanford.edu
