2 citations · 2 across the 6 of their papers we have counts for
7 papers
Market Incentives for AI Safety Investment
Nikita Tsoy
Despite their practical significance, modern AI systems pose significant societal risks, including toxicity, malicious use, and misinformation. Their mitigation depends not only on…
On Measuring Localization of Shortcuts in Deep Networks
Nikita Tsoy, Nikola Konstantinov
Shortcuts, spurious rules that perform well during training but fail to generalize, present a major challenge to the reliability of deep networks (Geirhos et al., 2020). However, t…
On the Impact of Performative Risk Minimization for Binary Random Variables
Nikita Tsoy, Ivan Kirev, Negin Rahimiyazdi +1
Performativity, the phenomenon where outcomes are influenced by predictions, is particularly prevalent in social contexts where individuals strategically respond to a deployed mode…
Incentivizing Truthful Collaboration in Heterogeneous Federated Learning
Dimitar Chakarov, Nikita Tsoy, Kristian Minchev +1
Federated learning (FL) is a distributed collaborative learning method, where multiple clients learn together by sharing gradient updates instead of raw data. However, it is well-k…
Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data
Nikita Tsoy, Nikola Konstantinov
Simplicity bias, the propensity of deep models to over-rely on simple features, has been identified as a potential reason for limited out-of-distribution generalization of neural n…
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains
Nikita Tsoy, Anna Mihalkova, Teodora Todorova +1
Cross-silo federated learning (FL) allows data owners to train accurate machine learning models by benefiting from each others private datasets. Unfortunately, the model accuracy b…
Strategic Data Sharing between Competitors
Nikita Tsoy, Nikola Konstantinov
Collaborative learning techniques have significantly advanced in recent years, enabling private model training across multiple organizations. Despite this opportunity, firms face a…