6 papers
Exact Instance Compression for Convex Empirical Risk Minimization via Color Refinement
Bryan Zhu, Ziang Chen
Empirical risk minimization (ERM) can be computationally expensive, with standard solvers scaling poorly even in the convex setting. We propose a novel lossless compression framewo…
Barron Space Representations for Elliptic PDEs with Homogeneous Boundary Conditions
Ziang Chen, Liqiang Huang
We study the complexity of approximating high-dimensional second-order elliptic PDEs with homogeneous boundary conditions on the unit hypercube using Barron spaces. Under suitable…
Randomized coordinate gradient descent almost surely escapes strict saddle points
Ziang Chen, Yingzhou Li, Zihao Li
We analyze the behavior of randomized coordinate gradient descent for nonconvex optimization, proving that under standard assumptions, the iterates almost surely escape strict sadd…
SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting
Shiwei Guo, Ziang Chen, Yupeng Ma +2
The Transformer model has shown strong performance in multivariate time series forecasting by leveraging channel-wise self-attention. However, this approach lacks temporal constrai…
On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles
Ziang Chen, Qiao Zhang, Runzhong Wang
Graph neural networks (GNNs) have been widely used in graph-related contexts. It is known that the separation power of GNNs is equivalent to that of the Weisfeiler-Lehman (WL) test…
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input
Ziang Chen, Rong Ge
In this work, we study the mean-field flow for learning subspace-sparse polynomials using stochastic gradient descent and two-layer neural networks, where the input distribution is…