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10/03/2022
CBLab: Scalable Traffic Simulation with Enriched Data Supporting
Traffic simulation provides interactive data for the optimization of tra...
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06/19/2021
Boosting Offline Reinforcement Learning with Residual Generative Modeling
Offline reinforcement learning (RL) tries to learn the near-optimal poli...
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05/20/2021
Objective-aware Traffic Simulation via Inverse Reinforcement Learning
Traffic simulators act as an essential component in the operating and pl...
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05/18/2021
Learning to Route via Theory-Guided Residual Network
The heavy traffic and related issues have always been concerns for moder...
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03/22/2021
Learning to Simulate on Sparse Trajectory Data
Simulation of the real-world traffic can be used to help validate the tr...
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12/31/2020
Automatic Historical Feature Generation through Tree-based Method in Ads Prediction
Historical features are important in ads click-through rate (CTR) predic...
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10/22/2020
Online Structured Meta-learning
Learning quickly is of great importance for machine intelligence deploye...
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08/01/2020
Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records
Recently, E-commerce platforms have extensive impacts on our human life....
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07/26/2020
Don't Overlook the Support Set: Towards Improving Generalization in Meta-learning
Meta-learning has proven to be a powerful paradigm for transferring the ...
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03/01/2020
Learning to Simulate Human Movement
Modeling how human moves on the space is useful for policy-making in tra...
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01/09/2020
A Probabilistic Simulator of Spatial Demand for Product Allocation
Connecting consumers with relevant products is a very important problem ...
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01/03/2020
Automated Relational Meta-learning
In order to efficiently learn with small amount of data on new tasks, me...
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11/26/2019
Few-Shot Knowledge Graph Completion
Knowledge graphs (KGs) serve as useful resources for various natural lan...
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10/07/2019
Graph Few-shot Learning via Knowledge Transfer
Towards the challenging problem of semi-supervised node classification, ...
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08/29/2019
Targeted Source Detection for Environmental Data
In the face of growing needs for water and energy, a fundamental underst...
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05/13/2019
Hierarchically Structured Meta-learning
In order to learn quickly with few samples, meta-learning utilizes prior...
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05/13/2019
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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05/12/2019
Learning Phase Competition for Traffic Signal Control
Increasingly available city data and advanced learning techniques have e...
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05/12/2019
Diagnosing Reinforcement Learning for Traffic Signal Control
With the increasing availability of traffic data and advance of deep rei...
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05/11/2019
CoLight: Learning Network-level Cooperation for Traffic Signal Control
Cooperation is critical in multi-agent reinforcement learning (MARL). In...
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04/17/2019
A Survey on Traffic Signal Control Methods
Traffic signal control is an important and challenging real-world proble...
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01/24/2019
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
Spatial-temporal prediction is a fundamental problem for constructing sm...
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08/26/2018
Detecting Outliers in Data with Correlated Measures
Advances in sensor technology have enabled the collection of large-scale...
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04/06/2018
Semantic Exploration of Traffic Dynamics
Given a large collection of urban datasets, how can we find their hidden...
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03/03/2018
Modeling Spatial-Temporal Dynamics for Traffic Prediction
Spatial-temporal prediction has many applications such as climate foreca...
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02/14/2012
