125 citations · 161 across the 8 of their papers we have counts for
12 papers
Differentially Private Estimation of Hawkes Process
Simiao Zuo, Tianyi Liu, Tuo Zhao +1
Point process models are of great importance in real world applications. In certain critical applications, estimation of point process models involves large amounts of sensitive pe…
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably
Tianyi Liu, Yan Li, Enlu Zhou +1
We investigate the role of noise in optimization algorithms for learning over-parameterized models. Specifically, we consider the recovery of a rank one matrix $Y^*\in R^{d\times d…
Accelerate Distributed Stochastic Descent for Nonconvex Optimization with Momentum
Guojing Cong, Tianyi Liu
Momentum method has been used extensively in optimizers for deep learning. Recent studies show that distributed training through K-step averaging has many nice properties. We propo…
GODSAC*: Graph Optimized DSAC* for Robot Relocalization
Alphonsus Adu-Bredu, Noah Del Coro, Tianyi Liu
Deep learning based camera pose estimation from monocular camera images has seen a recent uptake in Visual SLAM research. Even though such pose estimation approaches have excellent…
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization
Tianyi Liu, Yan Li, Song Wei +2
Numerous empirical evidences have corroborated the importance of noise in nonconvex optimization problems. The theory behind such empirical observations, however, is still largely…
On Computation and Generalization of Generative Adversarial Imitation Learning
Minshuo Chen, Yizhou Wang, Tianyi Liu +4
Generative Adversarial Imitation Learning (GAIL) is a powerful and practical approach for learning sequential decision-making policies. Different from Reinforcement Learning (RL),…