no code implementations • 20 Jul 2020 • Simin Liu, Tianrui Liu, Ali Vakilian, Yulin Wan, David P. Woodruff
Despite the growing body of work on this paradigm, a noticeable omission is that the locations of the non-zero entries of previous algorithms were fixed, and only their values were learned.
no code implementations • 1 Jan 2021 • Simin Liu, Tianrui Liu, Ali Vakilian, Yulin Wan, David Woodruff
In this work, we consider the problem of optimizing sketches to obtain low approximation error over a data distribution.
no code implementations • 23 Dec 2020 • Simin Liu
In recent years, deep learning has become more and more mature, and as a commonly used algorithm in deep learning, convolutional neural networks have been widely used in various visual tasks.
no code implementations • 11 Jun 2023 • Yi Li, Honghao Lin, Simin Liu, Ali Vakilian, David P. Woodruff
We fix this issue and propose approaches for learning a sketching matrix for both low-rank approximation and Hessian approximation for second order optimization.
no code implementations • 1 Nov 2023 • Simin Liu, Kai S. Yun, John M. Dolan, Changliu Liu
Our raCBFs are currently the most effective way to guarantee safety for uncertain systems, achieving 100% safety and up to 55% performance improvement over a robust baseline.
1 code implementation • 20 Nov 2022 • Simin Liu, Changliu Liu, John Dolan
We propose new methods to synthesize control barrier function (CBF)-based safe controllers that avoid input saturation, which can cause safety violations.
2 code implementations • ICLR 2019 • Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn
Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time.