7 Publications

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[7]
2022 | Thesis | IST-REx-ID: 10799 | OA
Robustness and fairness in machine learning
N.H. Konstantinov, Robustness and Fairness in Machine Learning, IST Austria, 2022.
View | Files available | DOI
 
[6]
2022 | Journal Article | IST-REx-ID: 10802 | OA
Fairness-aware PAC learning from corrupted data
N.H. Konstantinov, C. Lampert, Journal of Machine Learning Research 23 (2022) 1–60.
View | Files available | arXiv
 
[5]
2021 | Preprint | IST-REx-ID: 10803 | OA
Fairness through regularization for learning to rank
N.H. Konstantinov, C. Lampert, ArXiv (n.d.).
View | Files available | Download Preprint (ext.) | arXiv
 
[4]
2020 | Conference Paper | IST-REx-ID: 8724 | OA
On the sample complexity of adversarial multi-source PAC learning
N.H. Konstantinov, E. Frantar, D.-A. Alistarh, C. Lampert, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 5416–5425.
View | Files available | arXiv
 
[3]
2019 | Conference Paper | IST-REx-ID: 6590 | OA
Robust learning from untrusted sources
N.H. Konstantinov, C. Lampert, in:, Proceedings of the 36th International Conference on Machine Learning, PMLR, 2019, pp. 3488–3498.
View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2018 | Conference Paper | IST-REx-ID: 5962 | OA
The convergence of stochastic gradient descent in asynchronous shared memory
D.-A. Alistarh, C. De Sa, N.H. Konstantinov, in:, Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 169–178.
View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2018 | Conference Paper | IST-REx-ID: 6589 | OA
The convergence of sparsified gradient methods
D.-A. Alistarh, T. Hoefler, M. Johansson, N.H. Konstantinov, S. Khirirat, C. Renggli, in:, Advances in Neural Information Processing Systems 31, Neural information processing systems, 2018, pp. 5973–5983.
View | Download Preprint (ext.) | arXiv
 

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7 Publications

Mark all

[7]
2022 | Thesis | IST-REx-ID: 10799 | OA
Robustness and fairness in machine learning
N.H. Konstantinov, Robustness and Fairness in Machine Learning, IST Austria, 2022.
View | Files available | DOI
 
[6]
2022 | Journal Article | IST-REx-ID: 10802 | OA
Fairness-aware PAC learning from corrupted data
N.H. Konstantinov, C. Lampert, Journal of Machine Learning Research 23 (2022) 1–60.
View | Files available | arXiv
 
[5]
2021 | Preprint | IST-REx-ID: 10803 | OA
Fairness through regularization for learning to rank
N.H. Konstantinov, C. Lampert, ArXiv (n.d.).
View | Files available | Download Preprint (ext.) | arXiv
 
[4]
2020 | Conference Paper | IST-REx-ID: 8724 | OA
On the sample complexity of adversarial multi-source PAC learning
N.H. Konstantinov, E. Frantar, D.-A. Alistarh, C. Lampert, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 5416–5425.
View | Files available | arXiv
 
[3]
2019 | Conference Paper | IST-REx-ID: 6590 | OA
Robust learning from untrusted sources
N.H. Konstantinov, C. Lampert, in:, Proceedings of the 36th International Conference on Machine Learning, PMLR, 2019, pp. 3488–3498.
View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2018 | Conference Paper | IST-REx-ID: 5962 | OA
The convergence of stochastic gradient descent in asynchronous shared memory
D.-A. Alistarh, C. De Sa, N.H. Konstantinov, in:, Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 169–178.
View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2018 | Conference Paper | IST-REx-ID: 6589 | OA
The convergence of sparsified gradient methods
D.-A. Alistarh, T. Hoefler, M. Johansson, N.H. Konstantinov, S. Khirirat, C. Renggli, in:, Advances in Neural Information Processing Systems 31, Neural information processing systems, 2018, pp. 5973–5983.
View | Download Preprint (ext.) | arXiv
 

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