154 citations · 228 across the 7 of their papers we have counts for
6 papers · 1 filter
Locally Differentially Private Distributed Deep Learning via Knowledge Distillation
Di Zhuang, Mingchen Li, J. Morris Chang
Deep learning often requires a large amount of data. In real-world applications, e.g., healthcare applications, the data collected by a single organization (e.g., hospital) is ofte…
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…
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?
Yao-Chun Chan, Mingchen Li, Samet Oymak
Active learning is the set of techniques for intelligently labeling large unlabeled datasets to reduce the labeling effort. In parallel, recent developments in self-supervised and…
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…
Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian
Samet Oymak, Zalan Fabian, Mingchen Li +1
Modern neural network architectures often generalize well despite containing many more parameters than the size of the training dataset. This paper explores the generalization capa…
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks
Mingchen Li, Mahdi Soltanolkotabi, Samet Oymak
Modern neural networks are typically trained in an over-parameterized regime where the parameters of the model far exceed the size of the training data. Such neural networks in pri…