Konstantinov, Nikola

17 publications

ICLRW 2025 Measuring the Layer-Wise Impact of Image Shortcuts on Deep Model Features Nikita Tsoy, Nikola Konstantinov

ICML 2025 On the Impact of Performative Risk Minimization for Binary Random Variables Nikita Tsoy, Ivan Kirev, Negin Rahimiyazdi, Nikola Konstantinov

NeurIPSW 2024 Incentivizing Truthful Collaboration in Heterogeneous Federated Learning Dimitar Chakarov, Nikita Tsoy, Kristian Minchev, Nikola Konstantinov

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

ICML 2024 Simplicity Bias of Two-Layer Networks Beyond Linearly Separable Data Nikita Tsoy, Nikola Konstantinov

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

ICMLW 2023 Incentivizing Honesty Among Competitors in Collaborative Learning Florian E. Dorner, Nikola Konstantinov, Georgi Stoyanov Pashaliev, Martin Vechev

NeurIPS 2023 Incentivizing Honesty Among Competitors in Collaborative Learning and Optimization Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin Vechev

NeurIPS 2023 Strategic Data Sharing Between Competitors Nikita Tsoy, Nikola Konstantinov

ICMLW 2023 Strategic Data Sharing Between Competitors Nikita Tsoy, Nikola Konstantinov

TMLR 2022 Data Leakage in Federated Averaging Dimitar Iliev Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin Vechev

TMLR 2022 FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data Eugenia Iofinova, Nikola Konstantinov, Christoph H Lampert

JMLR 2022 Fairness-Aware PAC Learning from Corrupted Data Nikola Konstantinov, Christoph H. Lampert

NeurIPSW 2022 Generating Intuitive Fairness Specifications for Natural Language Processing Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin Vechev

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

ICML 2019 Robust Learning from Untrusted Sources Nikola Konstantinov, Christoph Lampert

NeurIPS 2018 The Convergence of Sparsified Gradient Methods Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cedric Renggli