4 citations · 10 across the 7 of their papers we have counts for
7 papers · 1 filter
Smoothed Agnostic Learning of Halfspaces over the Hypercube
Yiwen Kou, Raghu Meka
Agnostic learning of Boolean halfspaces is a fundamental problem in computational learning theory, but it is known to be computationally hard even for weak learning. Recent work [C…
Matching the Statistical Query Lower Bound for -Sparse Parity Problems with Sign Stochastic Gradient Descent
Yiwen Kou, Zixiang Chen, Quanquan Gu +1
The -sparse parity problem is a classical problem in computational complexity and algorithmic theory, serving as a key benchmark for understanding computational classes. In this…
Guided Discrete Diffusion for Electronic Health Record Generation
Jun Han, Zixiang Chen, Yongqian Li +4
Electronic health records (EHRs) are a pivotal data source that enables numerous applications in computational medicine, e.g., disease progression prediction, clinical trial design…
Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time
Zixiang Chen, Huizhuo Yuan, Yongqian Li +3
Discrete diffusion models have emerged as powerful tools for high-quality data generation. Despite their success in discrete spaces, such as text generation tasks, the acceleration…
Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data
Yiwen Kou, Zixiang Chen, Quanquan Gu
The implicit bias towards solutions with favorable properties is believed to be a key reason why neural networks trained by gradient-based optimization can generalize well. While t…
Why Does Sharpness-Aware Minimization Generalize Better Than SGD?
Zixiang Chen, Junkai Zhang, Yiwen Kou +3
The challenge of overfitting, in which the model memorizes the training data and fails to generalize to test data, has become increasingly significant in the training of large neur…
Benign Overfitting for Two-layer ReLU Convolutional Neural Networks
Yiwen Kou, Zixiang Chen, Yuanzhou Chen +1
Modern deep learning models with great expressive power can be trained to overfit the training data but still generalize well. This phenomenon is referred to as \textit{benign over…