16 citations · 18 across the 7 of their papers we have counts for
8 papers
Iterative Policy Refinement through Semantic Rollout Analysis
Feiyu Gavin Zhu, Qi Xu, Zhifei Deng +4
Structured policies improve efficiency, robustness, and interpretability in imitation learning by introducing task-specific inductive bias, but existing structure generation method…
Session-Level Dynamic Ad Load Optimization using Offline Robust Reinforcement Learning
Tao Liu, Qi Xu, Wei Shi +2
Session-level dynamic ad load optimization aims to personalize the density and types of delivered advertisements in real time during a user's online session by dynamically balancin…
Ads Supply Personalization via Doubly Robust Learning
Wei Shi, Chen Fu, Qi Xu +5
Ads supply personalization aims to balance the revenue and user engagement, two long-term objectives in social media ads, by tailoring the ad quantity and density. In the industry-…
A Practical Distributed ADMM Solver for Billion-Scale Generalized Assignment Problems
Jun Zhou, Feng Qi, Zhigang Hua +5
Assigning items to owners is a common problem found in various real-world applications, for example, audience-channel matching in marketing campaigns, borrower-lender matching in l…
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Runzhong Wang, Zhigang Hua, Gan Liu +6
Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature. Traditionally such problems are approximately solved with heuris…
Learning to Schedule DAG Tasks
Zhigang Hua, Feng Qi, Gan Liu +1
Scheduling computational tasks represented by directed acyclic graphs (DAGs) is challenging because of its complexity. Conventional scheduling algorithms rely heavily on simple heu…
Variational Optimization for the Submodular Maximum Coverage Problem
Jian Du, Zhigang Hua, Shuang Yang
We examine the \emph{submodular maximum coverage problem} (SMCP), which is related to a wide range of applications. We provide the first variational approximation for this problem…
Solving Billion-Scale Knapsack Problems
Xingwen Zhang, Feng Qi, Zhigang Hua +1
Knapsack problems (KPs) are common in industry, but solving KPs is known to be NP-hard and has been tractable only at a relatively small scale. This paper examines KPs in a slightl…