11 citations · 11 across the 5 of their papers we have counts for
6 papers
Learning-Theoretic Foundation for General Coded Computing: The Adversarial Setting
Parsa Moradi, Hanzaleh Akbarinodehi, Mohammad Ali Maddah-Ali
Adversarial workers pose a severe challenge in distributed computing systems by returning corrupted outputs that can substantially distort the recovered computations. Coded computi…
DReS: Dual Reconstruction Smoothing for Functional Regularization
Parsa Moradi, Tayyebeh Jahaninezhad, Hanzaleh Akbarinodehi +1
Smoothness is a key inductive bias in machine learning and is closely related to generalization. Existing smoothness-inducing methods typically rely either on explicit gradient reg…
Adversarial Robustness of Nonparametric Regression
Parsa Moradi, Hanzaleh Akabrinodehi, Mohammad Ali Maddah-Ali
In this paper, we investigate the adversarial robustness of nonparametric regression, a fundamental problem in machine learning, under the setting where an adversary can arbitraril…
General Coded Computing: Adversarial Settings
Parsa Moradi, Hanzaleh Akbarinodehi, Mohammad Ali Maddah-Ali
Conventional coded computing frameworks are predominantly tailored for structured computations, such as matrix multiplication and polynomial evaluation. Such tasks allow the reuse…
Game of Coding With an Unknown Adversary
Hanzaleh Akbarinodehi, Parsa Moradi, Mohammad Ali Maddah-Ali
Motivated by emerging decentralized applications, the \emph{game of coding} framework has been recently introduced to address scenarios where the adversary's control over coded sym…
Secure Coded Multi-Party Computation for Massive Matrix Operations
Hanzaleh Akbari Nodehi, Mohammad Ali Maddah-Ali
In this paper, we consider a secure multi-party computation problem (MPC), where the goal is to offload the computation of an arbitrary polynomial function of some massive private…