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Found 15 papers, 7 papers with code
Learning Correlated Stackelberg Equilibrium in General-Sum Multi-Leader-Single-Follower Games
Many real-world strategic games involve interactions between multiple players.
A Fast Algorithm for Onboard Atmospheric Powered Descent Guidance
Atmospheric powered descent guidance can be solved by successive convexification; however, its onboard application is impeded by the sharp increase in computation caused by nonlinear aerodynamic forces.
3D Human Motion Prediction: A Survey
3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors.
Robust Reinforcement Learning Under Minimax Regret for Green Security
We formulate the problem as a game between the defender and nature who controls the parameter values of the adversarial behavior and design an algorithm MIRROR to find a robust policy.
Contingency-Aware Influence Maximization: A Reinforcement Learning Approach
Empirical results show that our method achieves influence as high as the state-of-the-art methods for contingency-aware IM, while having negligible runtime at test phase.
AI-driven Prices for Externalities and Sustainability in Production Markets
Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-appropriating a common-pool resource (which diminishes future stock, and thus harvest, for everyone).
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning
In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes decision quality when the optimization is subsequently solved.
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning
In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes decision quality when the optimization is subsequently solved.
Aggregated Multi-GANs for Controlled 3D Human Motion Prediction
Our method is compelling in that it enables manipulable motion prediction across activity types and allows customization of the human movement in a variety of fine-grained ways.
Active Screening for Recurrent Diseases: A Reinforcement Learning Approach
However, given the limited number of health workers, only a small subset of the population can be visited in any given time period.
Motion Prediction Using Trajectory Cues
Predicting human motion from a historical pose sequence is at the core of many applications in computer vision.
EvaLDA: Efficient Evasion Attacks Towards Latent Dirichlet Allocation
As one of the most powerful topic models, Latent Dirichlet Allocation (LDA) has been used in a vast range of tasks, including document understanding, information retrieval and peer-reviewer assignment.
Learning Behaviors with Uncertain Human Feedback
However, previous works rarely consider the uncertainty when humans provide feedback, especially in cases that the optimal actions are not obvious to the trainers.
M2P2: Multimodal Persuasion Prediction using Adaptive Fusion
Our M2P2 (Multimodal Persuasion Prediction) framework is the first to use multimodal (acoustic, visual, language) data to solve the IPP problem.
An ensemble CNN method for biomedical entity normalization
Different representations of the same concept could often be seen in scientific reports and publications.
Ranked #2 on
Medical Concept Normalization
on BB-norm-phenotype
