4 citations · 8 across the 3 of their papers we have counts for
6 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…