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Found 15 papers, 2 papers with code
Gaussian Database Alignment and Gaussian Planted Matching
We study an instance of the database alignment problem with multivariate Gaussian features and derive results that apply both for database alignment and for planted matching, demonstrating the connection between them.
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker
Finding classifiers robust to adversarial examples is critical for their safe deployment.
Lower Bounds on the Robustness of Fixed Feature Extractors to Test-time Adversaries
In this paper, we develop a methodology to analyze the robustness of fixed feature extractors, which in turn provide bounds on the robustness of any classifier trained on top of it.
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries
In particular, it is critical to determine classifier-agnostic bounds on the training loss to establish when learning is possible.
Lower Bounds on Adversarial Robustness from Optimal Transport
In this paper, we use optimal transport to characterize the minimum possible loss in an adversarial classification scenario.
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples
A large body of recent work has investigated the phenomenon of evasion attacks using adversarial examples for deep learning systems, where the addition of norm-bounded perturbations to the test inputs leads to incorrect output classification.
Database Alignment with Gaussian Features
We consider the problem of aligning a pair of databases with jointly Gaussian features.
PAC-learning in the presence of adversaries
We then explicitly derive the adversarial VC-dimension for halfspace classifiers in the presence of a sample-wise norm-constrained adversary of the type commonly studied for evasion attacks and show that it is the same as the standard VC-dimension, closing an open question.
Partial Recovery of Erdős-Rényi Graph Alignment via $k$-Core Alignment
This estimator searches for an alignment in which the intersection of the correlated graphs using this alignment has a minimum degree of $k$.
PAC-learning in the presence of evasion adversaries
We then explicitly derive the adversarial VC-dimension for halfspace classifiers in the presence of a sample-wise norm-constrained adversary of the type commonly studied for evasion attacks and show that it is the same as the standard VC-dimension, closing an open question.
Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs
Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs.
Exact alignment recovery for correlated Erdős-Rényi graphs
We consider the problem of perfectly recovering the vertex correspondence between two correlated Erd\H{o}s-R\'enyi (ER) graphs on the same vertex set.
Enhancing Robustness of Machine Learning Systems via Data Transformations
We propose the use of data transformations as a defense against evasion attacks on ML classifiers.
On the Simultaneous Preservation of Privacy and Community Structure in Anonymized Networks
We ask the question "Does there exist a regime where the network cannot be deanonymized perfectly, yet the community structure could be learned?."
Improved Achievability and Converse Bounds for Erdős-Rényi Graph Matching
For a pair of correlated graphs on the same vertex set, the correspondence between the vertices can be obscured by randomly permuting the vertex labels of one of the graphs.
