Nikola Konstantinov

33 papers A* 9A 1Journal 22Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	LARP: Learner-Agnostic Robust Data Prefiltering. CoRR Kristian Minchev, Dimitar Iliev Dimitrov, Nikola Konstantinov
2025	J	`jnl`	On Measuring Localization of Shortcuts in Deep Networks. CoRR Nikita Tsoy, Nikola Konstantinov
2025	A*	`conf`	On the Impact of Performative Risk Minimization for Binary Random Variables. ICML Nikita Tsoy, Ivan Kirev, Negin Rahimiyazdi, Nikola Konstantinov
2025	J	`jnl`	On the Impact of Performative Risk Minimization for Binary Random Variables. CoRR Nikita Tsoy, Ivan Kirev, Negin Rahimiyazdi, Nikola Konstantinov
2025	J	`jnl`	PAC Learnability in the Presence of Performativity. CoRR Ivan Kirev, Lyuben Baltadzhiev, Nikola Konstantinov
2024	J	`jnl`	COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act. CoRR Philipp Guldimann, Alexander Spiridonov, Robin Staab, Nikola Jovanovic, Mark Vero, Velko Vechev, Anna Gueorguieva, Mislav Balunovic, Nikola Konstantinov, Pavol Bielik, Petar Tsankov, Martin T. Vechev
2024	J	`jnl`	Incentivizing Truthful Collaboration in Heterogeneous Federated Learning. CoRR Dimitar Chakarov, Nikita Tsoy, Kristian Minchev, Nikola Konstantinov
2024	A	`conf`	Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains. AISTATS Nikita Tsoy, Anna Mihalkova, Teodora N. Todorova, Nikola Konstantinov
2024	J	`jnl`	Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains. CoRR Nikita Tsoy, Anna Mihalkova, Teodora Todorova, Nikola Konstantinov
2024	A*	`conf`	Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data. ICML Nikita Tsoy, Nikola Konstantinov
2024	J	`jnl`	Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data. CoRR Nikita Tsoy, Nikola Konstantinov
2023	A*	`conf`	Human-Guided Fair Classification for Natural Language Processing. ICLR Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin T. Vechev
2023	A*	`conf`	Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. NeurIPS Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin T. Vechev
2023	J	`jnl`	Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. CoRR Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin T. Vechev
2023	A*	`conf`	Strategic Data Sharing between Competitors. NeurIPS Nikita Tsoy, Nikola Konstantinov
2023	J	`jnl`	Strategic Data Sharing between Competitors. CoRR Nikita Tsoy, Nikola Konstantinov
2022	J	`jnl`	Data Leakage in Federated Averaging. CoRR Dimitar I. Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin T. Vechev
2022	J	`jnl`	Data Leakage in Federated Averaging. Trans. Mach. Learn. Res. Dimitar Iliev Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin T. Vechev
2022	J	`jnl`	FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data. Trans. Mach. Learn. Res. Eugenia Iofinova, Nikola Konstantinov, Christoph H. Lampert
2022	J	`jnl`	Fairness-Aware PAC Learning from Corrupted Data. J. Mach. Learn. Res. Nikola Konstantinov, Christoph H. Lampert
2022	J	`jnl`	Human-Guided Fair Classification for Natural Language Processing. CoRR Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin T. Vechev
2021	J	`jnl`	FLEA: Provably Fair Multisource Learning from Unreliable Training Data. CoRR Eugenia Iofinova, Nikola Konstantinov, Christoph H. Lampert
2021	J	`jnl`	Fairness Through Regularization for Learning to Rank. CoRR Nikola Konstantinov, Christoph H. Lampert
2021	J	`jnl`	Fairness-Aware Learning from Corrupted Data. CoRR Nikola Konstantinov, Christoph H. Lampert
2021	—	`conf`	On the Impossibility of Fairness-Aware Learning from Corrupted Data. AFCR Nikola Konstantinov, Christoph H. Lampert
2020	A*	`conf`	On the Sample Complexity of Adversarial Multi-Source PAC Learning. ICML Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert
2020	J	`jnl`	On the Sample Complexity of Adversarial Multi-Source PAC Learning. CoRR Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph H. Lampert
2019	A*	`conf`	Robust Learning from Untrusted Sources. ICML Nikola Konstantinov, Christoph Lampert
2019	J	`jnl`	Robust Learning from Untrusted Sources. CoRR Nikola Konstantinov, Christoph Lampert
2018	A*	`conf`	The Convergence of Sparsified Gradient Methods. NeurIPS Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cédric Renggli
2018	J	`jnl`	The Convergence of Sparsified Gradient Methods. CoRR Dan Alistarh, Torsten Hoefler, Mikael Johansson, Sarit Khirirat, Nikola Konstantinov, Cédric Renggli
2018	A*	`conf`	The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. PODC Dan Alistarh, Christopher De Sa, Nikola Konstantinov
2018	J	`jnl`	The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. CoRR Dan Alistarh, Christopher De Sa, Nikola Konstantinov