14 citations · 23 across the 6 of their papers we have counts for
6 papers
Learning to Rank For Push Notifications Using Pairwise Expected Regret
Yuguang Yue, Yuanpu Xie, Huasen Wu +4
Listwise ranking losses have been widely studied in recommender systems. However, new paradigms of content consumption present new challenges for ranking methods. In this work we c…
Implicit Distributional Reinforcement Learning
Yuguang Yue, Zhendong Wang, Mingyuan Zhou
To improve the sample efficiency of policy-gradient based reinforcement learning algorithms, we propose implicit distributional actor-critic (IDAC) that consists of a distributiona…
Discrete Action On-Policy Learning with Action-Value Critic
Yuguang Yue, Yunhao Tang, Mingzhang Yin +1
Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension, making it cha…
Semi-supervised Learning using Adversarial Training with Good and Bad Samples
Wenyuan Li, Zichen Wang, Yuguang Yue +4
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training. Previous results have illustrated that generative adversarial netwo…
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables
Mingzhang Yin, Yuguang Yue, Mingyuan Zhou
To address the challenge of backpropagating the gradient through categorical variables, we propose the augment-REINFORCE-swap-merge (ARSM) gradient estimator that is unbiased and h…
T-optimal designs for multi-factor polynomial regression models via a semidefinite relaxation method
Yuguang Yue, Lieven Vandenberghe, Weng Kee Wong
We consider T-optimal experiment design problems for discriminating multi-factor polynomial regression models where the design space is defined by polynomial inequalities and the r…