1 citations · 1 across the 8 of their papers we have counts for
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DIVEBATCH: Accelerating Model Training Through Gradient-Diversity Aware Batch Size Adaptation
Yuen Chen, Yian Wang, Hari Sundaram
The goal of this paper is to accelerate the training of machine learning models, a critical challenge since the training of large-scale deep neural models can be computationally ex…
On the Necessity of Output Distribution Reweighting for Effective Class Unlearning
Ali Ebrahimpour-Boroojeny, Yian Wang, Hari Sundaram
In this paper, we reveal a significant shortcoming in class unlearning evaluations: overlooking the underlying class geometry can cause information leakage about the forgotten clas…
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…
Spectrum Extraction and Clipping for Implicitly Linear Layers
Ali Ebrahimpour Boroojeny, Matus Telgarsky, Hari Sundaram
We show the effectiveness of automatic differentiation in efficiently and correctly computing and controlling the spectrum of implicitly linear operators, a rich family of layer ty…