5 citations · 9 across the 9 of their papers we have counts for
10 papers
Pearl: A Production-ready Reinforcement Learning Agent
Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari +12
Reinforcement learning (RL) is a versatile framework for optimizing long-term goals. Although many real-world problems can be formalized with RL, learning and deploying a performan…
Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning
Ruiyang Xu, Jalaj Bhandari, Dmytro Korenkevych +4
Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected retur…
AMOM: Adaptive Masking over Masking for Conditional Masked Language Model
Yisheng Xiao, Ruiyang Xu, Lijun Wu +4
Transformer-based autoregressive (AR) methods have achieved appealing performance for varied sequence-to-sequence generation tasks, e.g., neural machine translation, summarization,…
Online Sparse Streaming Feature Selection Using Adapted Classification
RuiYang Xu, Di Wu, Xin Luo
Traditional feature selections need to know the feature space before learning, and online streaming feature selection (OSFS) is proposed to process streaming features on the fly. E…
A Validation Tool for Designing Reinforcement Learning Environments
Ruiyang Xu, Zhengxing Chen
Reinforcement learning (RL) has gained increasing attraction in the academia and tech industry with launches to a variety of impactful applications and products. Although research…
Semantic Parsing Natural Language into Relational Algebra
Ruiyang Xu, Ayush Singh
Natural interface to database (NLIDB) has been researched a lot during the past decades. In the core of NLIDB, is a semantic parser used to convert natural language into SQL. Solut…
Dual Monte Carlo Tree Search
Prashank Kadam, Ruiyang Xu, Karl Lieberherr
AlphaZero, using a combination of Deep Neural Networks and Monte Carlo Tree Search (MCTS), has successfully trained reinforcement learning agents in a tabula-rasa way. The neural M…
Solving QSAT problems with neural MCTS
Ruiyang Xu, Karl Lieberherr
Recent achievements from AlphaZero using self-play has shown remarkable performance on several board games. It is plausible to think that self-play, starting from zero knowledge, c…
First-Order Problem Solving through Neural MCTS based Reinforcement Learning
Ruiyang Xu, Prashank Kadam, Karl Lieberherr
The formal semantics of an interpreted first-order logic (FOL) statement can be given in Tarskian Semantics or a basically equivalent Game Semantics. The latter maps the statement…
Learning Self-Game-Play Agents for Combinatorial Optimization Problems
Ruiyang Xu, Karl Lieberherr
Recent progress in reinforcement learning (RL) using self-game-play has shown remarkable performance on several board games (e.g., Chess and Go) as well as video games (e.g., Atari…