163 citations · 1.6k across the 92 of their papers we have counts for
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NEON+: Accelerated Gradient Methods for Extracting Negative Curvature for Non-Convex Optimization
Yi Xu, Rong Jin, Tianbao Yang
Accelerated gradient (AG) methods are breakthroughs in convex optimization, improving the convergence rate of the gradient descent method for optimization with smooth functions. Ho…
First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time
Yi Xu, Rong Jin, Tianbao Yang
Two classes of methods have been proposed for escaping from saddle points with one using the second-order information carried by the Hessian and the other adding the noise into the…
Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM
Cong Leng, Hao Li, Shenghuo Zhu +1
Although deep learning models are highly effective for various learning tasks, their high computational costs prohibit the deployment to scenarios where either memory or computatio…
Empirical Risk Minimization for Stochastic Convex Optimization: - and -type of Risk Bounds
Lijun Zhang, Tianbao Yang, Rong Jin
Although there exist plentiful theories of empirical risk minimization (ERM) for supervised learning, current theoretical understandings of ERM for a related problem---stochastic c…
Dynamic Regret of Strongly Adaptive Methods
Lijun Zhang, Tianbao Yang, Rong Jin +1
To cope with changing environments, recent developments in online learning have introduced the concepts of adaptive regret and dynamic regret independently. In this paper, we illus…