90 citations · 152 across the 8 of their papers we have counts for
12 papers
AutoBalance: Optimized Loss Functions for Imbalanced Data
Mingchen Li, Xuechen Zhang, Christos Thrampoulidis +2
Imbalanced datasets are commonplace in modern machine learning problems. The presence of under-represented classes or groups with sensitive attributes results in concerns about gen…
Label-Imbalanced and Group-Sensitive Classification under Overparameterization
Ganesh Ramachandra Kini, Orestis Paraskevas, Samet Oymak +1
The goal in label-imbalanced and group-sensitive classification is to optimize relevant metrics such as balanced error and equal opportunity. Classical methods, such as weighted cr…
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks
Xiangyu Chang, Yingcong Li, Samet Oymak +1
Deep networks are typically trained with many more parameters than the size of the training dataset. Recent empirical evidence indicates that the practice of overparameterization n…
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View
Christos Thrampoulidis, Samet Oymak, Mahdi Soltanolkotabi
Contemporary machine learning applications often involve classification tasks with many classes. Despite their extensive use, a precise understanding of the statistical properties…
Exploring Weight Importance and Hessian Bias in Model Pruning
Mingchen Li, Yahya Sattar, Christos Thrampoulidis +1
Model pruning is an essential procedure for building compact and computationally-efficient machine learning models. A key feature of a good pruning algorithm is that it accurately…
The Generalized Lasso for Sub-gaussian Measurements with Dithered Quantization
Christos Thrampoulidis, Ankit Singh Rawat
In the problem of structured signal recovery from high-dimensional linear observations, it is commonly assumed that full-precision measurements are available. Under this assumption…