Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Saxena, Eshika"'
Modular addition is, on its face, a simple operation: given $N$ elements in $\mathbb{Z}_q$, compute their sum modulo $q$. Yet, scalable machine learning solutions to this problem remain elusive: prior work trains ML models that sum $N \le 6$ elements
Externí odkaz:
http://arxiv.org/abs/2410.03569
Lattice cryptography schemes based on the learning with errors (LWE) hardness assumption have been standardized by NIST for use as post-quantum cryptosystems, and by HomomorphicEncryption.org for encrypted compute on sensitive data. Thus, understandi
Externí odkaz:
http://arxiv.org/abs/2408.00882
Autor:
Stevens, Samuel, Wenger, Emily, Li, Cathy, Nolte, Niklas, Saxena, Eshika, Charton, François, Lauter, Kristin
Learning with Errors (LWE) is a hard math problem underlying recently standardized post-quantum cryptography (PQC) systems for key exchange and digital signatures. Prior work proposed new machine learning (ML)-based attacks on LWE problems with small
Externí odkaz:
http://arxiv.org/abs/2402.01082
Autor:
Agarwal, Chirag, Ley, Dan, Krishna, Satyapriya, Saxena, Eshika, Pawelczyk, Martin, Johnson, Nari, Puri, Isha, Zitnik, Marinka, Lakkaraju, Himabindu
While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introduce OpenXAI, a comprehensive and extensible open-source framework for
Externí odkaz:
http://arxiv.org/abs/2206.11104
Autor:
Agarwal, Chirag, Johnson, Nari, Pawelczyk, Martin, Krishna, Satyapriya, Saxena, Eshika, Zitnik, Marinka, Lakkaraju, Himabindu
As attribution-based explanation methods are increasingly used to establish model trustworthiness in high-stakes situations, it is critical to ensure that these explanations are stable, e.g., robust to infinitesimal perturbations to an input. However
Externí odkaz:
http://arxiv.org/abs/2203.06877