6 papers · 1 filter
Distributionally Robust K-Means Clustering
Vikrant Malik, Taylan Kargin, Babak Hassibi
K-means clustering is a workhorse of unsupervised learning, but it is notoriously brittle to outliers, distribution shifts, and limited sample sizes. Viewing k-means as Lloyd--Max…
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…
Optimal Implicit Bias in Linear Regression
Kanumuri Nithin Varma, Babak Hassibi
Most modern learning problems are over-parameterized, where the number of learnable parameters is much greater than the number of training data points. In this over-parameterized r…
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…
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…
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…