papers

Publications (9)

cs.SI2020

Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks

Adam Breuer, Roee Eilat, Udi Weinsberg

In this paper, we study the problem of early detection of fake user accounts on social networks based solely on their network connectivity with other users. Removing such accounts…

cs.LG2025

E-LDA: Toward Interpretable LDA Topic Models with Strong Guarantees in Logarithmic Parallel Time

Adam Breuer

In this paper, we provide the first practical algorithms with provable guarantees for the problem of inferring the topics assigned to each document in an LDA topic model. This is t…

stat.AP2026

Causal Inference with Video Features as Treatments

Kentaro Nakamura, Adam Breuer, Michael H. Crespin +2

We develop the first statistical methodology for causal inference with video features as treatments. Video is the most engaging content modality on the internet. A central causal q…

cs.CV2024

Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures

Sayanton V. Dibbo, Adam Breuer, Juston Moore +1

Recent model inversion attack algorithms permit adversaries to reconstruct a neural network's private and potentially sensitive training data by repeatedly querying the network. In…

cs.LG2026

Reducing information dependency does not cause training data privacy. Adversarially non-robust features do

Rasmus Torp, Shailen K. Smith, Adam Breuer

The paper shows that training data privacy against model inversion attacks is not due to memorization but to the presence of non‑robust adversarial features, and introduces a train…

#privacy#adversarial robustness#model inversion attacks#non-robust features
cs.DS2018

Non-monotone Submodular Maximization in Exponentially Fewer Iterations

Eric Balkanski, Adam Breuer, Yaron Singer

In this paper we consider parallelization for applications whose objective can be expressed as maximizing a non-monotone submodular function under a cardinality constraint. Our mai…