354 citations · 2.1k across the 86 of their papers we have counts for
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Hyperparameter Learning via Distributional Transfer
Ho Chung Leon Law, Peilin Zhao, Lucian Chan +2
Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We pro…
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Zebang Shen, Aryan Mokhtari, Tengfei Zhou +2
Recently, the decentralized optimization problem is attracting growing attention. Most existing methods are deterministic with high per-iteration cost and have a convergence rate q…
Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling
Peilin Zhao, Tong Zhang
Stochastic Gradient Descent (SGD) is a popular optimization method which has been applied to many important machine learning tasks such as Support Vector Machines and Deep Neural N…
Stochastic Optimization with Importance Sampling
Peilin Zhao, Tong Zhang
Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Gradient Descent (prox-SGD) and Proximal Stoc…
Adaptive Stochastic Alternating Direction Method of Multipliers
Peilin Zhao, Jinwei Yang, Tong Zhang +1
The Alternating Direction Method of Multipliers (ADMM) has been studied for years. The traditional ADMM algorithm needs to compute, at each iteration, an (empirical) expected loss…