The Convergence of Sparsified Gradient Methods. - Dan Alistarh; Torsten Hoefler; Mikael Johansson 001; Nikola Konstantinov; Sarit Khirirat; Cédric Renggli (NeurIPS, 2018)

The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. - Dan Alistarh; Christopher De Sa; Nikola Konstantinov (PODC, 2018)

Robust Learning from Untrusted Sources. - Nikola Konstantinov; Christoph Lampert (ICML, 2019)

On the Sample Complexity of Adversarial Multi-Source PAC Learning. - Nikola Konstantinov; Elias Frantar; Dan Alistarh; Christoph Lampert (ICML, 2020)

On the Impossibility of Fairness-Aware Learning from Corrupted Data. - Nikola Konstantinov; Christoph H. Lampert (AFCR, 2021)

Fairness Through Regularization for Learning to Rank. - Nikola Konstantinov; Christoph H. Lampert (CoRR, 2021)

Fairness-Aware Learning from Corrupted Data. - Nikola Konstantinov; Christoph H. Lampert (CoRR, 2021)

FLEA: Provably Fair Multisource Learning from Unreliable Training Data. - Eugenia Iofinova; Nikola Konstantinov; Christoph H. Lampert (CoRR, 2021)

Fairness-Aware PAC Learning from Corrupted Data. - Nikola Konstantinov; Christoph H. Lampert (J. Mach. Learn. Res., 2022)

Data Leakage in Federated Averaging. - Dimitar Iliev Dimitrov; Mislav Balunovic; Nikola Konstantinov; Martin T. Vechev (Trans. Mach. Learn. Res., 2022)

FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data. - Eugenia Iofinova; Nikola Konstantinov; Christoph H. Lampert (Trans. Mach. Learn. Res., 2022)

Human-Guided Fair Classification for Natural Language Processing. - Florian E. Dorner; Momchil Peychev; Nikola Konstantinov; Naman Goel; Elliott Ash; Martin T. Vechev (ICLR, 2023)

Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. - Florian E. Dorner; Nikola Konstantinov; Georgi Pashaliev; Martin T. Vechev (NeurIPS, 2023)

Strategic Data Sharing between Competitors. - Nikita Tsoy; Nikola Konstantinov (NeurIPS, 2023)

Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains. - Nikita Tsoy; Anna Mihalkova; Teodora N. Todorova; Nikola Konstantinov (AISTATS, 2024)

Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data. - Nikita Tsoy; Nikola Konstantinov (ICML, 2024)

COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act. - Philipp Guldimann; Alexander Spiridonov; Robin Staab; Nikola Jovanovic 001; Mark Vero; Velko Vechev; Anna Gueorguieva; Mislav Balunovic; Nikola Konstantinov; Pavol Bielik; Petar Tsankov; Martin T. Vechev (CoRR, 2024)

Incentivizing Truthful Collaboration in Heterogeneous Federated Learning. - Dimitar Chakarov; Nikita Tsoy; Kristian Minchev; Nikola Konstantinov (CoRR, 2024)

On the Impact of Performative Risk Minimization for Binary Random Variables. - Nikita Tsoy; Ivan Kirev; Negin Rahimiyazdi; Nikola Konstantinov (CoRR, 2025)

LARP: Learner-Agnostic Robust Data Prefiltering. - Kristian Minchev; Dimitar Iliev Dimitrov; Nikola Konstantinov (CoRR, 2025)