124 citations · 129 across the 6 of their papers we have counts for
8 papers
Stochastic Ratios Tracking Algorithm for Large Scale Machine Learning Problems
Shigeng Sun, Yuchen Xie
Many machine learning applications and tasks rely on the stochastic gradient descent (SGD) algorithm and its variants. Effective step length selection is crucial for the success of…
Match Cutting: Finding Cuts with Smooth Visual Transitions
Boris Chen, Amir Ziai, Rebecca Tucker +1
A match cut is a transition between a pair of shots that uses similar framing, composition, or action to fluidly bring the viewer from one scene to the next. Match cuts are frequen…
Adaptive Finite-Difference Interval Estimation for Noisy Derivative-Free Optimization
Hao-Jun Michael Shi, Yuchen Xie, Melody Qiming Xuan +1
A common approach for minimizing a smooth nonlinear function is to employ finite-difference approximations to the gradient. While this can be easily performed when no error is pres…
Constrained and Composite Optimization via Adaptive Sampling Methods
Yuchen Xie, Raghu Bollapragada, Richard Byrd +1
The motivation for this paper stems from the desire to develop an adaptive sampling method for solving constrained optimization problems in which the objective function is stochast…
A Noise-Tolerant Quasi-Newton Algorithm for Unconstrained Optimization
Hao-Jun Michael Shi, Yuchen Xie, Richard Byrd +1
This paper describes an extension of the BFGS and L-BFGS methods for the minimization of a nonlinear function subject to errors. This work is motivated by applications that contain…
Analysis of the BFGS Method with Errors
Yuchen Xie, Richard Byrd, Jorge Nocedal
The classical convergence analysis of quasi-Newton methods assumes that the function and gradients employed at each iteration are exact. In this paper, we consider the case when th…
A Theoretical Analysis of Deep Q-Learning
Jianqing Fan, Zhaoran Wang, Yuchen Xie +1
Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well understood. In this work, we make the first attempt to theoretically und…
On the convergence of BFGS on a class of piecewise linear non-smooth functions
Yuchen Xie, Andreas Waechter
The quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method has proven to be very reliable and efficient for the minimization of smooth objective functions since its inception…