1 citations · 1 across the 10 of their papers we have counts for
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From Switching to Dynamic Regret: A Simple Reduction via Unbiased Random Sequences
Yibo Wang, Wenhao Yang, Sifan Yang +3
In non-stationary online learning, dynamic regret has attracted increasing attention as a measure of how well an online learner performs against a time-varying comparator sequence.…
Distributed Online Convex Optimization with Efficient Communication: Improved Algorithm and Lower bounds
Sifan Yang, Wenhao Yang, Wei Jiang +1
We investigate distributed online convex optimization with compressed communication, where learners connected by a network collaboratively minimize a sequence of global loss fu…
Dual Adaptivity: Universal Algorithms for Minimizing the Adaptive Regret of Convex Functions
Lijun Zhang, Wenhao Yang, Guanghui Wang +2
To deal with changing environments, a new performance measure -- adaptive regret, defined as the maximum static regret over any interval, was proposed in online learning. Under the…
Discounted Online Convex Optimization: Uniform Regret Across a Continuous Interval
Wenhao Yang, Sifan Yang, Lijun Zhang
Reflecting the greater significance of recent history over the distant past in non-stationary environments, -discounted regret has been introduced in online convex optimization…
Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction
Wei Jiang, Sifan Yang, Wenhao Yang +1
Sign stochastic gradient descent (signSGD) is a communication-efficient method that transmits only the sign of stochastic gradients for parameter updating. Existing literature has…
Universal Online Convex Optimization with Projection per Round
Wenhao Yang, Yibo Wang, Peng Zhao +1
To address the uncertainty in function types, recent progress in online convex optimization (OCO) has spurred the development of universal algorithms that simultaneously attain min…