From the 1 of 9 linked papers with an AI index.
9 papers
A Cheeger Inequality for Size-Specific Conductance
Yufan Huang, David F. Gleich
The paper proposes a modified spectral cut for the μ‑conductance measure of a graph and proves a two‑sided Cheeger inequality that relates the optimal spectral solution to the size…
Suboptimality bounds for trace-bounded SDPs enable a faster and scalable low-rank SDP solver SDPLR+
Yufan Huang, David F. Gleich
Semidefinite programs (SDPs) and their solvers are powerful tools with many applications in machine learning and data science. Designing scalable SDP solvers is challenging because…
Can Entry-Wise Clipping Give Spectral Control of Stochastic Gradients?
Zitao Song, Cedar Site Bai, Zhe Zhang +2
Training instabilities such as loss spikes are frequently the result of stochastic gradient noise. Because of rare expressions in language training data, and multiple layer composi…
Decoupling Variance and Scale-Invariant Updates in Adaptive Gradient Descent for Unified Vector and Matrix Optimization
Zitao Song, Cedar Site Bai, Zhe Zhang +2
Adaptive methods like Adam have become the standard for large-scale vector and Euclidean optimization due to their coordinate-wise adaptation with a second-orde…
Dominant H-Eigenvectors of Tensor Kronecker Products Do Not Decouple
Ayush Kulkarni, Charles Colley, David F. Gleich
We illustrate a counterexample to an open question related to the dominant H-eigenvector of a Kronecker product of tensors. For matrices and Z-eigenvectors of tensors, the dominant…
Fault Oblivious Eigenvalue Solver
Jayanta Mukherjee, Xuejiao Kang, David F. Gleich +2
Eigenvalue problems serve as fundamental substrates for applications in large-scale scientific simulations and machine learning, often requiring computation on massively parallel p…