5 papers
Toward Reliable Machine Unlearning: Theory, Algorithms, and Evaluation
Ali Ebrahimpour-Boroojeny
We propose new methodologies for both unlearning random set of samples and class unlearning and show that they outperform existing methods. The main driver of our unlearning method…
Small Cues, Big Differences: Evaluating Interaction and Presentation for Annotation Retrieval in AR
Zahra Borhani, Ali Ebrahimpour-Boroojeny, Francisco R. Ortega
Augmented Reality (AR) enables intuitive interaction with virtual annotations overlaid on the real world, supporting a wide range of applications such as remote assistance, educati…
AMUN: Adversarial Machine UNlearning
Ali Ebrahimpour-Boroojeny, Hari Sundaram, Varun Chandrasekaran
Machine unlearning, where users can request the deletion of a forget dataset, is becoming increasingly important because of numerous privacy regulations. Initial works on ``exact''…
LOTOS: Layer-wise Orthogonalization for Training Robust Ensembles
Ali Ebrahimpour-Boroojeny, Hari Sundaram, Varun Chandrasekaran
Transferability of adversarial examples is a well-known property that endangers all classification models, even those that are only accessible through black-box queries. Prior work…
SARS-CoV-2 orthologs of pathogenesis-involved small viral RNAs of SARS-CoV
Ali Ebrahimpour Boroojeny, Hamidreza Chitsaz
Background: The COVID-19 pandemic clock is ticking and the survival of many of mankind's modern institutions and or survival of many individuals is at stake. There is a need for tr…