Nikola H Konstantinov
Graduate School
Lampert Group
9 Publications
2022 | Journal Article | IST-REx-ID: 12495 | 
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.
[Published Version]
View
| Files available
| Download Published Version (ext.)
| arXiv
2022 | Journal Article | IST-REx-ID: 10802 |
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.
[Published Version]
View
| Files available
| arXiv
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.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2022 | Thesis | IST-REx-ID: 10799 |
N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute of Science and Technology Austria, 2022.
[Published Version]
View
| Files available
| DOI
2021 | Preprint | IST-REx-ID: 10803 |
N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. .
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 8724 |
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.
[Published Version]
View
| Files available
| arXiv
2019 | Conference Paper | IST-REx-ID: 6590 |
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.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 5962 |
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.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2018 | Conference Paper | IST-REx-ID: 6589 |
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.
[Preprint]
View
| Download Preprint (ext.)
| WoS
| arXiv
9 Publications
2022 | Journal Article | IST-REx-ID: 12495 |
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.
[Published Version]
View
| Files available
| Download Published Version (ext.)
| arXiv
2022 | Journal Article | IST-REx-ID: 10802 |
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.
[Published Version]
View
| Files available
| arXiv
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.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2022 | Thesis | IST-REx-ID: 10799 |
N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute of Science and Technology Austria, 2022.
[Published Version]
View
| Files available
| DOI
2021 | Preprint | IST-REx-ID: 10803 |
N. H. Konstantinov and C. Lampert, “Fairness through regularization for learning to rank,” arXiv. .
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 8724 |
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.
[Published Version]
View
| Files available
| arXiv
2019 | Conference Paper | IST-REx-ID: 6590 |
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.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 5962 |
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.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2018 | Conference Paper | IST-REx-ID: 6589 |
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.
[Preprint]
View
| Download Preprint (ext.)
| WoS
| arXiv