Nikola H Konstantinov
Graduate School
Lampert Group
9 Publications
2022 | Journal Article | IST-REx-ID: 12495 | 
Iofinova, E. B., Konstantinov, N. H., & Lampert, C. (2022). FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research. ML Research Press.
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2022 | Journal Article | IST-REx-ID: 10802 |
Konstantinov, N. H., & Lampert, C. (2022). Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. ML Research Press.
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| arXiv
2022 | Conference Paper | IST-REx-ID: 13241 |
Konstantinov, N. H., & Lampert, C. (2022). On the impossibility of fairness-aware learning from corrupted data. In Proceedings of Machine Learning Research (Vol. 171, pp. 59–83). ML Research Press.
[Preprint]
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| arXiv
2022 | Thesis | IST-REx-ID: 10799 |
Konstantinov, N. H. (2022). Robustness and fairness in machine learning. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10799
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2021 | Preprint | IST-REx-ID: 10803 |
Konstantinov, N. H., & Lampert, C. (n.d.). Fairness through regularization for learning to rank. arXiv. https://doi.org/10.48550/arXiv.2102.05996
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2020 | Conference Paper | IST-REx-ID: 8724 |
Konstantinov, N. H., Frantar, E., Alistarh, D.-A., & Lampert, C. (2020). On the sample complexity of adversarial multi-source PAC learning. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 5416–5425). Online: ML Research Press.
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| arXiv
2019 | Conference Paper | IST-REx-ID: 6590 |
Konstantinov, N. H., & Lampert, C. (2019). Robust learning from untrusted sources. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 3488–3498). Long Beach, CA, USA: ML Research Press.
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2018 | Conference Paper | IST-REx-ID: 5962 |
Alistarh, D.-A., De Sa, C., & Konstantinov, N. H. (2018). The convergence of stochastic gradient descent in asynchronous shared memory. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18 (pp. 169–178). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212763
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2018 | Conference Paper | IST-REx-ID: 6589 |
Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., & Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural Information Processing Systems Foundation.
[Preprint]
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9 Publications
2022 | Journal Article | IST-REx-ID: 12495 |
Iofinova, E. B., Konstantinov, N. H., & Lampert, C. (2022). FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research. ML Research Press.
[Published Version]
View
| Files available
| Download Published Version (ext.)
| arXiv
2022 | Journal Article | IST-REx-ID: 10802 |
Konstantinov, N. H., & Lampert, C. (2022). Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. ML Research Press.
[Published Version]
View
| Files available
| arXiv
2022 | Conference Paper | IST-REx-ID: 13241 |
Konstantinov, N. H., & Lampert, C. (2022). On the impossibility of fairness-aware learning from corrupted data. In Proceedings of Machine Learning Research (Vol. 171, pp. 59–83). ML Research Press.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2022 | Thesis | IST-REx-ID: 10799 |
Konstantinov, N. H. (2022). Robustness and fairness in machine learning. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10799
[Published Version]
View
| Files available
| DOI
2021 | Preprint | IST-REx-ID: 10803 |
Konstantinov, N. H., & Lampert, C. (n.d.). Fairness through regularization for learning to rank. arXiv. https://doi.org/10.48550/arXiv.2102.05996
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 8724 |
Konstantinov, N. H., Frantar, E., Alistarh, D.-A., & Lampert, C. (2020). On the sample complexity of adversarial multi-source PAC learning. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 5416–5425). Online: ML Research Press.
[Published Version]
View
| Files available
| arXiv
2019 | Conference Paper | IST-REx-ID: 6590 |
Konstantinov, N. H., & Lampert, C. (2019). Robust learning from untrusted sources. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 3488–3498). Long Beach, CA, USA: ML Research Press.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 5962 |
Alistarh, D.-A., De Sa, C., & Konstantinov, N. H. (2018). The convergence of stochastic gradient descent in asynchronous shared memory. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18 (pp. 169–178). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212763
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2018 | Conference Paper | IST-REx-ID: 6589 |
Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., & Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural Information Processing Systems Foundation.
[Preprint]
View
| Download Preprint (ext.)
| WoS
| arXiv