Nikola H Konstantinov

Graduate School
Lampert Group

9 Publications

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[9]
2022 | Journal Article | IST-REx-ID: 12495 | OA
E. B. Iofinova, N. H. Konstantinov, and C. Lampert, “FLEA: Provably robust fair multisource learning from unreliable training data,” Transactions on Machine Learning Research. ML Research Press, 2022.
View | Files available | Download Published Version (ext.) | arXiv
 
[8]
2022 | Conference Paper | IST-REx-ID: 13241
N. H. Konstantinov and C. Lampert, “On the impossibility of fairness-aware learning from corrupted data,” in Proceedings of Machine Learning Research, 2022, vol. 171, pp. 59–83.
View | Files available | arXiv
 
[7]
2022 | Thesis | IST-REx-ID: 10799 | OA
N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute of Science and Technology Austria, 2022.
View | Files available | DOI
 
[6]
2022 | Journal Article | IST-REx-ID: 10802 | OA
N. H. Konstantinov and C. Lampert, “Fairness-aware PAC learning from corrupted data,” Journal of Machine Learning Research, vol. 23. ML Research Press, pp. 1–60, 2022.
View | Files available | arXiv
 
[5]
2021 | Preprint | IST-REx-ID: 10803 | OA
N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. .
View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[4]
2020 | Conference Paper | IST-REx-ID: 8724 | OA
N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425.
View | Files available | arXiv
 
[3]
2019 | Conference Paper | IST-REx-ID: 6590 | OA
N. H. Konstantinov and C. Lampert, “Robust learning from untrusted sources,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA, 2019, vol. 97, pp. 3488–3498.
View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2018 | Conference Paper | IST-REx-ID: 5962 | OA
D.-A. Alistarh, C. De Sa, and N. H. Konstantinov, “The convergence of stochastic gradient descent in asynchronous shared memory,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 169–178.
View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[1]
2018 | Conference Paper | IST-REx-ID: 6589 | OA
D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.
View | Download Preprint (ext.) | WoS | arXiv
 
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9 Publications

Mark all

[9]
2022 | Journal Article | IST-REx-ID: 12495 | OA
E. B. Iofinova, N. H. Konstantinov, and C. Lampert, “FLEA: Provably robust fair multisource learning from unreliable training data,” Transactions on Machine Learning Research. ML Research Press, 2022.
View | Files available | Download Published Version (ext.) | arXiv
 
[8]
2022 | Conference Paper | IST-REx-ID: 13241
N. H. Konstantinov and C. Lampert, “On the impossibility of fairness-aware learning from corrupted data,” in Proceedings of Machine Learning Research, 2022, vol. 171, pp. 59–83.
View | Files available | arXiv
 
[7]
2022 | Thesis | IST-REx-ID: 10799 | OA
N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute of Science and Technology Austria, 2022.
View | Files available | DOI
 
[6]
2022 | Journal Article | IST-REx-ID: 10802 | OA
N. H. Konstantinov and C. Lampert, “Fairness-aware PAC learning from corrupted data,” Journal of Machine Learning Research, vol. 23. ML Research Press, pp. 1–60, 2022.
View | Files available | arXiv
 
[5]
2021 | Preprint | IST-REx-ID: 10803 | OA
N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. .
View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[4]
2020 | Conference Paper | IST-REx-ID: 8724 | OA
N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425.
View | Files available | arXiv
 
[3]
2019 | Conference Paper | IST-REx-ID: 6590 | OA
N. H. Konstantinov and C. Lampert, “Robust learning from untrusted sources,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA, 2019, vol. 97, pp. 3488–3498.
View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2018 | Conference Paper | IST-REx-ID: 5962 | OA
D.-A. Alistarh, C. De Sa, and N. H. Konstantinov, “The convergence of stochastic gradient descent in asynchronous shared memory,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 169–178.
View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[1]
2018 | Conference Paper | IST-REx-ID: 6589 | OA
D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.
View | Download Preprint (ext.) | WoS | arXiv
 
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    Citation Style: IEEE

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