103 citations · 290 across the 30 of their papers we have counts for
9 papers · 1 filter
Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability
Yafei Wang, Bo Pan, Wei Tu +6
Sample average approximation (SAA), a popular method for tractably solving stochastic optimization problems, enjoys strong asymptotic performance guarantees in settings with indepe…
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation
Shiqi Yang, Yaxing Wang, Joost van de Weijer +2
Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible…
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization
Ke Sun, Yafei Wang, Yi Liu +5
Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL. Despite its…
Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture Search
Keith G. Mills, Fred X. Han, Jialin Zhang +6
Neural architecture search automates neural network design and has achieved state-of-the-art results in many deep learning applications. While recent literature has focused on desi…
LNAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning
Keith G. Mills, Fred X. Han, Mohammad Salameh +6
Neural architecture search (NAS) has achieved remarkable results in deep neural network design. Differentiable architecture search converts the search over discrete architectures i…
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations
Ke Sun, Yingnan Zhao, Shangling Jui +1
In real scenarios, state observations that an agent observes may contain measurement errors or adversarial noises, misleading the agent to take suboptimal actions or even collapse…