Nikola H Konstantinov

Graduate School
Lampert Group

9 Publications

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[9]
2022 | Published | Conference Paper | IST-REx-ID: 13241 | OA
Konstantinov NH, Lampert C. 2022. On the impossibility of fairness-aware learning from corrupted data. Proceedings of Machine Learning Research. vol. 171, 59–83.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[8]
2022 | Published | Journal Article | IST-REx-ID: 12495 | OA
Iofinova EB, Konstantinov NH, Lampert C. 2022. FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[7]
2022 | Published | Journal Article | IST-REx-ID: 10802 | OA
Konstantinov NH, Lampert C. 2022. Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. 23, 1–60.
[Published Version] View | Files available | arXiv
 
[6]
2022 | Published | Thesis | IST-REx-ID: 10799 | OA
Konstantinov NH. 2022. Robustness and fairness in machine learning. Institute of Science and Technology Austria.
[Published Version] View | Files available | DOI
 
[5]
2021 | Draft | Preprint | IST-REx-ID: 10803 | OA
Konstantinov NH, Lampert C. Fairness through regularization for learning to rank. arXiv, 2102.05996.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[4]
2020 | Published | Conference Paper | IST-REx-ID: 8724 | OA
Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. 2020. On the sample complexity of adversarial multi-source PAC learning. Proceedings of the 37th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 119, 5416–5425.
[Published Version] View | Files available | arXiv
 
[3]
2019 | Published | Conference Paper | IST-REx-ID: 6590 | OA
Konstantinov NH, Lampert C. 2019. Robust learning from untrusted sources. Proceedings of the 36th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 97, 3488–3498.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2018 | Published | Conference Paper | IST-REx-ID: 5962 | OA
Alistarh D-A, De Sa C, Konstantinov NH. 2018. The convergence of stochastic gradient descent in asynchronous shared memory. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. PODC: Principles of Distributed Computing, 169–178.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[1]
2018 | Published | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. 2018. The convergence of sparsified gradient methods. Advances in Neural Information Processing Systems 31. NeurIPS: Conference on Neural Information Processing Systems vol. Volume 2018, 5973–5983.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
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9 Publications

Mark all

[9]
2022 | Published | Conference Paper | IST-REx-ID: 13241 | OA
Konstantinov NH, Lampert C. 2022. On the impossibility of fairness-aware learning from corrupted data. Proceedings of Machine Learning Research. vol. 171, 59–83.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[8]
2022 | Published | Journal Article | IST-REx-ID: 12495 | OA
Iofinova EB, Konstantinov NH, Lampert C. 2022. FLEA: Provably robust fair multisource learning from unreliable training data. Transactions on Machine Learning Research.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[7]
2022 | Published | Journal Article | IST-REx-ID: 10802 | OA
Konstantinov NH, Lampert C. 2022. Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. 23, 1–60.
[Published Version] View | Files available | arXiv
 
[6]
2022 | Published | Thesis | IST-REx-ID: 10799 | OA
Konstantinov NH. 2022. Robustness and fairness in machine learning. Institute of Science and Technology Austria.
[Published Version] View | Files available | DOI
 
[5]
2021 | Draft | Preprint | IST-REx-ID: 10803 | OA
Konstantinov NH, Lampert C. Fairness through regularization for learning to rank. arXiv, 2102.05996.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[4]
2020 | Published | Conference Paper | IST-REx-ID: 8724 | OA
Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. 2020. On the sample complexity of adversarial multi-source PAC learning. Proceedings of the 37th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 119, 5416–5425.
[Published Version] View | Files available | arXiv
 
[3]
2019 | Published | Conference Paper | IST-REx-ID: 6590 | OA
Konstantinov NH, Lampert C. 2019. Robust learning from untrusted sources. Proceedings of the 36th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 97, 3488–3498.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2018 | Published | Conference Paper | IST-REx-ID: 5962 | OA
Alistarh D-A, De Sa C, Konstantinov NH. 2018. The convergence of stochastic gradient descent in asynchronous shared memory. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. PODC: Principles of Distributed Computing, 169–178.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[1]
2018 | Published | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. 2018. The convergence of sparsified gradient methods. Advances in Neural Information Processing Systems 31. NeurIPS: Conference on Neural Information Processing Systems vol. Volume 2018, 5973–5983.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
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Citation Style: ISTA Annual Report

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