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NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims at learning a good policy from ...
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Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Search
Hero drafting is essential in MOBA game playing as it builds the team of...
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Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
Cycle-consistent training is widely used for jointly learning a forward ...
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Sobolev Wasserstein GAN
Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) dua...
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Reciprocal Supervised Learning Improves Neural Machine Translation
Despite the recent success on image classification, self-training has on...
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U-rank: Utility-oriented Learning to Rank with Implicit Feedback
Learning to rank with implicit feedback is one of the most important tas...
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Efficient Projection-Free Algorithms for Saddle Point Problems
The Frank-Wolfe algorithm is a classic method for constrained optimizati...
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Model-based Policy Optimization with Unsupervised Model Adaptation
Model-based reinforcement learning methods learn a dynamics model with r...
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A Compare Aggregate Transformer for Understanding Document-grounded Dialogue
Unstructured documents serving as external knowledge of the dialogues he...
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GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning
The heavy traffic congestion problem has always been a concern for moder...
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GIKT: A Graph-based Interaction Model for Knowledge Tracing
With the rapid development in online education, knowledge tracing (KT) h...
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Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great...
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Glancing Transformer for Non-Autoregressive Neural Machine Translation
Non-autoregressive neural machine translation achieves remarkable infere...
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Bidirectional Model-based Policy Optimization
Model-based reinforcement learning approaches leverage a forward dynamic...
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Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
Interactive recommender system (IRS) has drawn huge attention because of...
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CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
Two important tasks at the intersection of knowledge graphs and natural ...
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Counterfactual Off-Policy Training for Neural Response Generation
Learning a neural response generation model on data synthesized under th...
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Energy-Based Imitation Learning
We tackle a common scenario in imitation learning (IL), where agents try...
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Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space
In this paper, we focus on reducing the labeled data size for sentence l...
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AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
Learning effective feature interactions is crucial for click-through rat...
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Large-Scale Optimal Transport via Adversarial Training with Cycle-Consistency
Recent advances in large-scale optimal transport have greatly extended i...
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Author Name Disambiguation on Heterogeneous Information Network with Adversarial Representation Learning
Author name ambiguity causes inadequacy and inconvenience in academic in...
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GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Molecular graph generation is a fundamental problem for drug discovery a...
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Multi-Agent Interactions Modeling with Correlated Policies
In multi-agent systems, complex interacting behaviors arise due to the h...
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Improving Unsupervised Domain Adaptation with Variational Information Bottleneck
Domain adaptation aims to leverage the supervision signal of source doma...
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Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling
Sequential recommendation task aims to predict user preference over item...
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Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching
Improving the efficiency of dispatching orders to vehicles is a research...
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Signal Instructed Coordination in Team Competition
Most existing models of multi-agent reinforcement learning (MARL) adopt ...
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Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning
In many real-world problems, a team of agents need to collaborate to max...
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Bi-level Actor-Critic for Multi-agent Coordination
Coordination is one of the essential problems in multi-agent systems. Ty...
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Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds
Most e-commerce product feeds provide blended results of advertised prod...
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Towards Making the Most of BERT in Neural Machine Translation
GPT-2 and BERT demonstrate the effectiveness of using pre-trained langua...
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An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation
This paper studies graph-based recommendation, where an interaction grap...
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CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
How to optimally dispatch orders to vehicles and how to trade off betwee...
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Triple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence
Knowledge base is one of the main forms to represent information in a st...
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Dynamically Fused Graph Network for Multi-hop Reasoning
Text-based question answering (TBQA) has been studied extensively in rec...
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CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
Traffic signal control is an emerging application scenario for reinforce...
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CoLight: Learning Network-level Cooperation for Traffic Signal Control
Cooperation is critical in multi-agent reinforcement learning (MARL). In...
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Deep Landscape Forecasting for Real-time Bidding Advertising
The emergence of real-time auction in online advertising has drawn huge ...
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Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
User response prediction, which models the user preference w.r.t. the pr...
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Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs
Lipschitz continuity recently becomes popular in generative adversarial ...
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Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space
In this paper we propose a hybrid architecture of actor-critic algorithm...
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Lipschitz Generative Adversarial Nets
In this paper we study the convergence of generative adversarial network...
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CommunityGAN: Community Detection with Generative Adversarial Nets
Community detection refers to the task of discovering groups of vertices...
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Large-scale Interactive Recommendation with Tree-structured Policy Gradient
Reinforcement learning (RL) has recently been introduced to interactive ...
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Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation
With the rapid growth of the express industry, intelligent warehouses th...
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AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods
Adam is shown not being able to converge to the optimal solution in cert...
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Retrieval-Enhanced Adversarial Training for Neural Response Generation
Dialogue systems are usually built on either generation-based or retriev...
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TGE-PS: Text-driven Graph Embedding with Pairs Sampling
In graphs with rich text information, constructing expressive graph repr...
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Factorized Q-Learning for Large-Scale Multi-Agent Systems
Deep Q-learning has achieved a significant success in single-agent decis...
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