5 papers · 1 filter
PAC Learnability in the Presence of Performativity
Ivan Kirev, Lyuben Baltadzhiev, Nikola Konstantinov
Following the wide-spread adoption of machine learning models in real-world applications, the phenomenon of performativity, i.e. model-dependent shifts in the test distribution, be…
LARP: Learner-Agnostic Robust Data Prefiltering
Kristian Minchev, Dimitar I. Dimitrov, Nikola Konstantinov
Public datasets, crucial for modern machine learning and statistical inference, often contain low-quality or contaminated samples that can harm model performance. This creates a ne…
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…
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…