12 papers
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…
Precise Performance of Linear Denoisers in the Proportional Regime
Reza Ghane, Danil Akhtiamov, Babak Hassibi
In the present paper we study the performance of linear denoisers for noisy data of the form , where is the desired data with…
Dual Space Preconditioning for Gradient Descent in the Overparameterized Regime
Reza Ghane, Danil Akhtiamov, Babak Hassibi
In this work, we study the convergence properties of the Dual Space Preconditioned Gradient Descent, encompassing optimizers such as Normalized Gradient Descent and Gradient Clippi…
Implicit Bias and Convergence of Matrix Stochastic Mirror Descent
Danil Akhtiamov, Reza Ghane, Omead Pooladzandi +1
We investigate Stochastic Mirror Descent (SMD) with matrix parameters and vector-valued predictions, a framework relevant to multi-class classification and matrix completion proble…
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…
A Precise Performance Analysis of the Randomized Singular Value Decomposition
Danil Akhtiamov, Reza Ghane, Babak Hassibi
The Randomized Singular Value Decomposition (RSVD) is a widely used algorithm for efficiently computing low-rank approximations of large matrices, without the need to construct a f…