17 papers
Incremental Aggregation on the Grassmannian for Asynchronous Eigenspace Computation
Xiaolu Wang, Jiang Hu, Hoi-To Wai
We study asynchronous optimization for finite-sum eigenspace computation in heterogeneous distributed systems. The theoretical foundations for asynchronous eigenspace computation r…
Learning Graph Topology with Functional Priors via Bilevel Optimization
Chenyue Zhang, Shangyuan Liu, Hoi-To Wai +1
Learning graph topology of complex networks is challenging due to limited data availability and imprecise data models. Different from prior works that focus on structural priors wi…
EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization
Chung-Yiu Yau, Dawei Li, Athanasios Glentis +3
Lookahead-based acceleration methods, such as Nesterov's momentum, are widely used in optimization, but they often become unreliable in deep learning training mainly due to stochas…
Byzantine-Resilient Decentralized Online Resource Allocation
Runhua Wang, Qing Ling, Hoi-To Wai +1
In this paper, we investigate the problem of decentralized online resource allocation in the presence of Byzantine attacks. In this problem setting, some agents may be compromised…
Revisiting the Constant Stepsize Stochastic Approximation with Decision-Dependent Markovian Noise
Hadi Hadavi, Wenlong Mou, Sergey Samsonov +1
We revisit the convergence analysis of constant stepsize stochastic approximation (SA) with decision-dependent Markovian noise, with a focus on characterizing the stationary bias a…
Decentralized Learning with Dynamically Refined Edge Weights: A Data-Dependent Framework
Rongxing Du, Hoi-To Wai
This paper aims to accelerate decentralized optimization by strategically designing the edge weights used in the agent-to-agent message exchanges. We propose a Dynamic Directed Dec…