125 citations · 182 across the 14 of their papers we have counts for
15 papers
Machine Learning Force Fields with Data Cost Aware Training
Alexander Bukharin, Tianyi Liu, Shengjie Wang +4
Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation, which finds widespread applications in chemistry and biomedical research.…
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…
Bayesian Stochastic Gradient Descent for Stochastic Optimization with Streaming Input Data
Tianyi Liu, Yifan Lin, Enlu Zhou
We consider stochastic optimization under distributional uncertainty, where the unknown distributional parameter is estimated from streaming data that arrive sequentially over time…
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),…
Online Quantification of Input Model Uncertainty by Two-Layer Importance Sampling
Tianyi Liu, Enlu Zhou
Stochastic simulation has been widely used to analyze the performance of complex stochastic systems and facilitate decision making in those systems. Stochastic simulation is driven…
Towards Understanding the Importance of Shortcut Connections in Residual Networks
Tianyi Liu, Minshuo Chen, Mo Zhou +3
Residual Network (ResNet) is undoubtedly a milestone in deep learning. ResNet is equipped with shortcut connections between layers, and exhibits efficient training using simple fir…
Towards Understanding the Importance of Noise in Training Neural Networks
Mo Zhou, Tianyi Liu, Yan Li +3
Numerous empirical evidence has corroborated that the noise plays a crucial rule in effective and efficient training of neural networks. The theory behind, however, is still largel…
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization
Tianyi Liu, Shiyang Li, Jianping Shi +2
Asynchronous momentum stochastic gradient descent algorithms (Async-MSGD) is one of the most popular algorithms in distributed machine learning. However, its convergence properties…
A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization
Tianyi Liu, Zhehui Chen, Enlu Zhou +1
Momentum Stochastic Gradient Descent (MSGD) algorithm has been widely applied to many nonconvex optimization problems in machine learning, e.g., training deep neural networks, vari…
Risk Quantification in Stochastic Simulation under Input Uncertainty
Helin Zhu, Tianyi Liu, Enlu Zhou
When simulating a complex stochastic system, the behavior of output response depends on input parameters estimated from finite real-world data, and the finiteness of data brings in…
Implementation of Training Convolutional Neural Networks
Tianyi Liu, Shuangsang Fang, Yuehui Zhao +2
Deep learning refers to the shining branch of machine learning that is based on learning levels of representations. Convolutional Neural Networks (CNN) is one kind of deep neural n…