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2020 – today
- 2022
[i8]Dimitar I. Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin T. Vechev:
Data Leakage in Federated Averaging. CoRR abs/2206.12395 (2022)- 2021
- [i7]Nikola Konstantinov, Christoph H. Lampert:
Fairness Through Regularization for Learning to Rank. CoRR abs/2102.05996 (2021) - [i6]Nikola Konstantinov, Christoph H. Lampert:
Fairness-Aware Learning from Corrupted Data. CoRR abs/2102.06004 (2021) - [i5]Eugenia Iofinova, Nikola Konstantinov, Christoph H. Lampert:
FLEA: Provably Fair Multisource Learning from Unreliable Training Data. CoRR abs/2106.11732 (2021) - 2020
- [c4]Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert:
On the Sample Complexity of Adversarial Multi-Source PAC Learning. ICML 2020: 5416-5425 - [i4]Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph H. Lampert:
On the Sample Complexity of Adversarial Multi-Source PAC Learning. CoRR abs/2002.10384 (2020)
2010 – 2019
- 2019
- [c3]Nikola Konstantinov, Christoph Lampert:
Robust Learning from Untrusted Sources. ICML 2019: 3488-3498 - [i3]Nikola Konstantinov, Christoph Lampert:
Robust Learning from Untrusted Sources. CoRR abs/1901.10310 (2019) - 2018
- [c2]Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cédric Renggli:
The Convergence of Sparsified Gradient Methods. NeurIPS 2018: 5977-5987 - [c1]Dan Alistarh, Christopher De Sa, Nikola Konstantinov:
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. PODC 2018: 169-178 - [i2]Dan Alistarh, Christopher De Sa, Nikola Konstantinov:
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. CoRR abs/1803.08841 (2018) - [i1]Dan Alistarh, Torsten Hoefler, Mikael Johansson, Sarit Khirirat, Nikola Konstantinov, Cédric Renggli:
The Convergence of Sparsified Gradient Methods. CoRR abs/1809.10505 (2018)
Coauthor Index
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