6 citations · 13 across the 15 of their papers we have counts for
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One-Bit Quantization for Random Features Models
Danil Akhtiamov, Reza Ghane, Babak Hassibi
Recent advances in neural networks have led to significant computational and memory demands, spurring interest in one-bit weight compression to enable efficient inference on resour…
Universality in Transfer Learning for Linear Models
Reza Ghane, Danil Akhtiamov, Babak Hassibi
We study the problem of transfer learning and fine-tuning in linear models for both regression and binary classification. In particular, we consider the use of stochastic gradient…
One-Bit Quantization and Sparsification for Multiclass Linear Classification with Strong Regularization
Reza Ghane, Danil Akhtiamov, Babak Hassibi
We study the use of linear regression for multiclass classification in the over-parametrized regime where some of the training data is mislabeled. In such scenarios it is necessary…
A Novel Gaussian Min-Max Theorem and its Applications
Danil Akhtiamov, David Bosch, Reza Ghane +2
A celebrated result by Gordon allows one to compare the min-max behavior of two Gaussian processes if certain inequality conditions are met. The consequences of this result include…
Regularized Linear Regression for Binary Classification
Danil Akhtiamov, Reza Ghane, Babak Hassibi
Regularized linear regression is a promising approach for binary classification problems in which the training set has noisy labels since the regularization term can help to avoid…
The Generalization Error of Stochastic Mirror Descent on Over-Parametrized Linear Models
Danil Akhtiamov, Babak Hassibi
Despite being highly over-parametrized, and having the ability to fully interpolate the training data, deep networks are known to generalize well to unseen data. It is now understo…